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images
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DictionaryFormationNV.m
DictionaryFormationNV.m
ProductQuantizationNV.m
ProductQuantizationNV.m
ProductQuantizerNV.asv
ProductQuantizerNV.asv
ProductQuantizerNV.m
ProductQuantizerNV.m
README.md
README.md
SIFTnormalizationNV.m
SIFTnormalizationNV.m
SplittingPhase.m
SplittingPhase.m
VLAD.m
VLAD.m
VLADNV.m
VLADNV.m
VLAD_plotting.m
VLAD_plotting.m
cvpr2010_jegou_douze_v1845.tar.gz
cvpr2010_jegou_douze_v1845.tar.gz
denseSIFTNV.asv
denseSIFTNV.asv
denseSIFTNV.m
denseSIFTNV.m
gaussNV.m
gaussNV.m
inria_fisher_v1.tgz
inria_fisher_v1.tgz
miniBatchKMeansNV.m
miniBatchKMeansNV.m
mustBePowerOfTwo.asv
mustBePowerOfTwo.asv
mustBePowerOfTwo.m
mustBePowerOfTwo.m
splitTheDatastore.m
splitTheDatastore.m
summaryTable.csv
summaryTable.csv
yael-master.zip
yael-master.zip

Repository files navigation

Image Classification using VLAD in MATLAB

This project implements an image classification pipeline using Vector of Locally Aggregated Descriptors (VLAD). The pipeline involves extracting Dense SIFT features, forming a dictionary, encoding features using VLAD, and training an SVM classifier with various hyperparameter optimizations. The goal is to achieve high accuracy in image classification by leveraging the VLAD encoding scheme.

Εικόνα8

Enlarged view of a Voronoi cell with local descriptors and the cell center. Dotted lines depict the residuals between the local descriptors and the respective center.

Project Structure

  • VLAD.m: Main script to run the project, including data loading, feature extraction, dictionary formation, VLAD encoding, and classification.
  • denseSIFTNV.m: Extracts Dense SIFT features from the dataset.
  • DictionaryFormationNV.m: Forms a dictionary using the extracted features.
  • VLADNV.m: Encodes features using VLAD.
  • splitTheDatastore.m: Splits the image datastore into training and testing sets.

How to Run

To run this project:

  1. Ensure MATLAB is installed on your system.
  2. Clone this repository to your local machine.
  3. Place your dataset in a directory of your choice.
  4. Open MATLAB and navigate to the cloned project directory.
  5. Run the VLAD.m script to start the image classification pipeline.
run('VLAD.m')

License

This code is for teaching/research purposes only.

About

This project implements an image classification pipeline using Vector of Locally Aggregated Descriptors.

Topics

computer-visionmatlabvlad

Resources

Readme
Activity

Stars

0 stars

Watchers

1 watching

Forks

0 forks
Report repository

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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images
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scene_categories
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.gitignore
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DictionaryFormationNV.m
DictionaryFormationNV.m
ProductQuantizationNV.m
ProductQuantizationNV.m
ProductQuantizerNV.asv
ProductQuantizerNV.asv
ProductQuantizerNV.m
ProductQuantizerNV.m
README.md
README.md
SIFTnormalizationNV.m
SIFTnormalizationNV.m
SplittingPhase.m
SplittingPhase.m
VLAD.m
VLAD.m
VLADNV.m
VLADNV.m
VLAD_plotting.m
VLAD_plotting.m
cvpr2010_jegou_douze_v1845.tar.gz
cvpr2010_jegou_douze_v1845.tar.gz
denseSIFTNV.asv
denseSIFTNV.asv
denseSIFTNV.m
denseSIFTNV.m
gaussNV.m
gaussNV.m
inria_fisher_v1.tgz
inria_fisher_v1.tgz
miniBatchKMeansNV.m
miniBatchKMeansNV.m
mustBePowerOfTwo.asv
mustBePowerOfTwo.asv
mustBePowerOfTwo.m
mustBePowerOfTwo.m
splitTheDatastore.m
splitTheDatastore.m
summaryTable.csv
summaryTable.csv
yael-master.zip
yael-master.zip
Repository files navigation
Image Classification using VLAD in MATLAB
This project implements an image classification pipeline using Vector of Locally Aggregated Descriptors (VLAD).
The pipeline involves extracting Dense SIFT features, forming a dictionary, encoding features using VLAD, and training an SVM classifier with various hyperparameter optimizations.
The goal is to achieve high accuracy in image classification by leveraging the VLAD encoding scheme.
Εικόνα8
Enlarged view of a Voronoi cell with local descriptors and the cell center.
Dotted lines depict the residuals between the local descriptors and the respective center.
Project Structure
  • VLAD.m: Main script to run the project, including data loading, feature extraction, dictionary formation, VLAD encoding, and classification.
  • denseSIFTNV.m: Extracts Dense SIFT features from the dataset.
  • DictionaryFormationNV.m: Forms a dictionary using the extracted features.
  • VLADNV.m: Encodes features using VLAD.
  • splitTheDatastore.m: Splits the image datastore into training and testing sets.
How to Run
To run this project:
  1. Ensure MATLAB is installed on your system.
  2. Clone this repository to your local machine.
  3. Place your dataset in a directory of your choice.
  4. Open MATLAB and navigate to the cloned project directory.
  5. Run the VLAD.m script to start the image classification pipeline.
run('VLAD.m')
License
This code is for teaching/research purposes only.
About
This project implements an image classification pipeline using Vector of Locally Aggregated Descriptors.
Topics
computer-visionmatlabvlad
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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images
images
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scene_categories
.gitattributes
.gitattributes
.gitignore
.gitignore
DictionaryFormationNV.m
DictionaryFormationNV.m
ProductQuantizationNV.m
ProductQuantizationNV.m
ProductQuantizerNV.asv
ProductQuantizerNV.asv
ProductQuantizerNV.m
ProductQuantizerNV.m
README.md
README.md
SIFTnormalizationNV.m
SIFTnormalizationNV.m
SplittingPhase.m
SplittingPhase.m
VLAD.m
VLAD.m
VLADNV.m
VLADNV.m
VLAD_plotting.m
VLAD_plotting.m
cvpr2010_jegou_douze_v1845.tar.gz
cvpr2010_jegou_douze_v1845.tar.gz
denseSIFTNV.asv
denseSIFTNV.asv
denseSIFTNV.m
denseSIFTNV.m
gaussNV.m
gaussNV.m
inria_fisher_v1.tgz
inria_fisher_v1.tgz
miniBatchKMeansNV.m
miniBatchKMeansNV.m
mustBePowerOfTwo.asv
mustBePowerOfTwo.asv
mustBePowerOfTwo.m
mustBePowerOfTwo.m
splitTheDatastore.m
splitTheDatastore.m
summaryTable.csv
summaryTable.csv
yael-master.zip
yael-master.zip
Repository files navigation
Image Classification using VLAD in MATLAB
This project implements an image classification pipeline using Vector of Locally Aggregated Descriptors (VLAD).
The pipeline involves extracting Dense SIFT features, forming a dictionary, encoding features using VLAD, and training an SVM classifier with various hyperparameter optimizations.
The goal is to achieve high accuracy in image classification by leveraging the VLAD encoding scheme.
Εικόνα8
Enlarged view of a Voronoi cell with local descriptors and the cell center.
Dotted lines depict the residuals between the local descriptors and the respective center.
Project Structure
  • VLAD.m: Main script to run the project, including data loading, feature extraction, dictionary formation, VLAD encoding, and classification.
  • denseSIFTNV.m: Extracts Dense SIFT features from the dataset.
  • DictionaryFormationNV.m: Forms a dictionary using the extracted features.
  • VLADNV.m: Encodes features using VLAD.
  • splitTheDatastore.m: Splits the image datastore into training and testing sets.
How to Run
To run this project:
  1. Ensure MATLAB is installed on your system.
  2. Clone this repository to your local machine.
  3. Place your dataset in a directory of your choice.
  4. Open MATLAB and navigate to the cloned project directory.
  5. Run the VLAD.m script to start the image classification pipeline.
run('VLAD.m')
License
This code is for teaching/research purposes only.
About
This project implements an image classification pipeline using Vector of Locally Aggregated Descriptors.
Topics
computer-visionmatlabvlad
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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images
images
scene_categories
scene_categories
.gitattributes
.gitattributes
.gitignore
.gitignore
DictionaryFormationNV.m
DictionaryFormationNV.m
ProductQuantizationNV.m
ProductQuantizationNV.m
ProductQuantizerNV.asv
ProductQuantizerNV.asv
ProductQuantizerNV.m
ProductQuantizerNV.m
README.md
README.md
SIFTnormalizationNV.m
SIFTnormalizationNV.m
SplittingPhase.m
SplittingPhase.m
VLAD.m
VLAD.m
VLADNV.m
VLADNV.m
VLAD_plotting.m
VLAD_plotting.m
cvpr2010_jegou_douze_v1845.tar.gz
cvpr2010_jegou_douze_v1845.tar.gz
denseSIFTNV.asv
denseSIFTNV.asv
denseSIFTNV.m
denseSIFTNV.m
gaussNV.m
gaussNV.m
inria_fisher_v1.tgz
inria_fisher_v1.tgz
miniBatchKMeansNV.m
miniBatchKMeansNV.m
mustBePowerOfTwo.asv
mustBePowerOfTwo.asv
mustBePowerOfTwo.m
mustBePowerOfTwo.m
splitTheDatastore.m
splitTheDatastore.m
summaryTable.csv
summaryTable.csv
yael-master.zip
yael-master.zip
Repository files navigation
Image Classification using VLAD in MATLAB
This project implements an image classification pipeline using Vector of Locally Aggregated Descriptors (VLAD).
The pipeline involves extracting Dense SIFT features, forming a dictionary, encoding features using VLAD, and training an SVM classifier with various hyperparameter optimizations.
The goal is to achieve high accuracy in image classification by leveraging the VLAD encoding scheme.
Εικόνα8
Enlarged view of a Voronoi cell with local descriptors and the cell center.
Dotted lines depict the residuals between the local descriptors and the respective center.
Project Structure
  • VLAD.m: Main script to run the project, including data loading, feature extraction, dictionary formation, VLAD encoding, and classification.
  • denseSIFTNV.m: Extracts Dense SIFT features from the dataset.
  • DictionaryFormationNV.m: Forms a dictionary using the extracted features.
  • VLADNV.m: Encodes features using VLAD.
  • splitTheDatastore.m: Splits the image datastore into training and testing sets.
How to Run
To run this project:
  1. Ensure MATLAB is installed on your system.
  2. Clone this repository to your local machine.
  3. Place your dataset in a directory of your choice.
  4. Open MATLAB and navigate to the cloned project directory.
  5. Run the VLAD.m script to start the image classification pipeline.
run('VLAD.m')
License
This code is for teaching/research purposes only.
About
This project implements an image classification pipeline using Vector of Locally Aggregated Descriptors.
Topics
computer-visionmatlabvlad
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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images
images
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scene_categories
.gitattributes
.gitattributes
.gitignore
.gitignore
DictionaryFormationNV.m
DictionaryFormationNV.m
ProductQuantizationNV.m
ProductQuantizationNV.m
ProductQuantizerNV.asv
ProductQuantizerNV.asv
ProductQuantizerNV.m
ProductQuantizerNV.m
README.md
README.md
SIFTnormalizationNV.m
SIFTnormalizationNV.m
SplittingPhase.m
SplittingPhase.m
VLAD.m
VLAD.m
VLADNV.m
VLADNV.m
VLAD_plotting.m
VLAD_plotting.m
cvpr2010_jegou_douze_v1845.tar.gz
cvpr2010_jegou_douze_v1845.tar.gz
denseSIFTNV.asv
denseSIFTNV.asv
denseSIFTNV.m
denseSIFTNV.m
gaussNV.m
gaussNV.m
inria_fisher_v1.tgz
inria_fisher_v1.tgz
miniBatchKMeansNV.m
miniBatchKMeansNV.m
mustBePowerOfTwo.asv
mustBePowerOfTwo.asv
mustBePowerOfTwo.m
mustBePowerOfTwo.m
splitTheDatastore.m
splitTheDatastore.m
summaryTable.csv
summaryTable.csv
yael-master.zip
yael-master.zip
Repository files navigation
Image Classification using VLAD in MATLAB
This project implements an image classification pipeline using Vector of Locally Aggregated Descriptors (VLAD).
The pipeline involves extracting Dense SIFT features, forming a dictionary, encoding features using VLAD, and training an SVM classifier with various hyperparameter optimizations.
The goal is to achieve high accuracy in image classification by leveraging the VLAD encoding scheme.
Εικόνα8
Enlarged view of a Voronoi cell with local descriptors and the cell center.
Dotted lines depict the residuals between the local descriptors and the respective center.
Project Structure
  • VLAD.m: Main script to run the project, including data loading, feature extraction, dictionary formation, VLAD encoding, and classification.
  • denseSIFTNV.m: Extracts Dense SIFT features from the dataset.
  • DictionaryFormationNV.m: Forms a dictionary using the extracted features.
  • VLADNV.m: Encodes features using VLAD.
  • splitTheDatastore.m: Splits the image datastore into training and testing sets.
How to Run
To run this project:
  1. Ensure MATLAB is installed on your system.
  2. Clone this repository to your local machine.
  3. Place your dataset in a directory of your choice.
  4. Open MATLAB and navigate to the cloned project directory.
  5. Run the VLAD.m script to start the image classification pipeline.
run('VLAD.m')
License
This code is for teaching/research purposes only.
About
This project implements an image classification pipeline using Vector of Locally Aggregated Descriptors.
Topics
computer-visionmatlabvlad
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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.vscode
.vscode
images
images
scene_categories
scene_categories
.gitattributes
.gitattributes
.gitignore
.gitignore
DictionaryFormationNV.m
DictionaryFormationNV.m
ProductQuantizationNV.m
ProductQuantizationNV.m
ProductQuantizerNV.asv
ProductQuantizerNV.asv
ProductQuantizerNV.m
ProductQuantizerNV.m
README.md
README.md
SIFTnormalizationNV.m
SIFTnormalizationNV.m
SplittingPhase.m
SplittingPhase.m
VLAD.m
VLAD.m
VLADNV.m
VLADNV.m
VLAD_plotting.m
VLAD_plotting.m
cvpr2010_jegou_douze_v1845.tar.gz
cvpr2010_jegou_douze_v1845.tar.gz
denseSIFTNV.asv
denseSIFTNV.asv
denseSIFTNV.m
denseSIFTNV.m
gaussNV.m
gaussNV.m
inria_fisher_v1.tgz
inria_fisher_v1.tgz
miniBatchKMeansNV.m
miniBatchKMeansNV.m
mustBePowerOfTwo.asv
mustBePowerOfTwo.asv
mustBePowerOfTwo.m
mustBePowerOfTwo.m
splitTheDatastore.m
splitTheDatastore.m
summaryTable.csv
summaryTable.csv
yael-master.zip
yael-master.zip
Repository files navigation
Image Classification using VLAD in MATLAB
This project implements an image classification pipeline using Vector of Locally Aggregated Descriptors (VLAD).
The pipeline involves extracting Dense SIFT features, forming a dictionary, encoding features using VLAD, and training an SVM classifier with various hyperparameter optimizations.
The goal is to achieve high accuracy in image classification by leveraging the VLAD encoding scheme.
Εικόνα8
Enlarged view of a Voronoi cell with local descriptors and the cell center.
Dotted lines depict the residuals between the local descriptors and the respective center.
Project Structure
  • VLAD.m: Main script to run the project, including data loading, feature extraction, dictionary formation, VLAD encoding, and classification.
  • denseSIFTNV.m: Extracts Dense SIFT features from the dataset.
  • DictionaryFormationNV.m: Forms a dictionary using the extracted features.
  • VLADNV.m: Encodes features using VLAD.
  • splitTheDatastore.m: Splits the image datastore into training and testing sets.
How to Run
To run this project:
  1. Ensure MATLAB is installed on your system.
  2. Clone this repository to your local machine.
  3. Place your dataset in a directory of your choice.
  4. Open MATLAB and navigate to the cloned project directory.
  5. Run the VLAD.m script to start the image classification pipeline.
run('VLAD.m')
License
This code is for teaching/research purposes only.
About
This project implements an image classification pipeline using Vector of Locally Aggregated Descriptors.
Topics
computer-visionmatlabvlad
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
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Contributors
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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.vscode
.vscode
images
images
scene_categories
scene_categories
.gitattributes
.gitattributes
.gitignore
.gitignore
DictionaryFormationNV.m
DictionaryFormationNV.m
ProductQuantizationNV.m
ProductQuantizationNV.m
ProductQuantizerNV.asv
ProductQuantizerNV.asv
ProductQuantizerNV.m
ProductQuantizerNV.m
README.md
README.md
SIFTnormalizationNV.m
SIFTnormalizationNV.m
SplittingPhase.m
SplittingPhase.m
VLAD.m
VLAD.m
VLADNV.m
VLADNV.m
VLAD_plotting.m
VLAD_plotting.m
cvpr2010_jegou_douze_v1845.tar.gz
cvpr2010_jegou_douze_v1845.tar.gz
denseSIFTNV.asv
denseSIFTNV.asv
denseSIFTNV.m
denseSIFTNV.m
gaussNV.m
gaussNV.m
inria_fisher_v1.tgz
inria_fisher_v1.tgz
miniBatchKMeansNV.m
miniBatchKMeansNV.m
mustBePowerOfTwo.asv
mustBePowerOfTwo.asv
mustBePowerOfTwo.m
mustBePowerOfTwo.m
splitTheDatastore.m
splitTheDatastore.m
summaryTable.csv
summaryTable.csv
yael-master.zip
yael-master.zip
Repository files navigation
Image Classification using VLAD in MATLAB
This project implements an image classification pipeline using Vector of Locally Aggregated Descriptors (VLAD).
The pipeline involves extracting Dense SIFT features, forming a dictionary, encoding features using VLAD, and training an SVM classifier with various hyperparameter optimizations.
The goal is to achieve high accuracy in image classification by leveraging the VLAD encoding scheme.
Εικόνα8
Enlarged view of a Voronoi cell with local descriptors and the cell center.
Dotted lines depict the residuals between the local descriptors and the respective center.
Project Structure
  • VLAD.m: Main script to run the project, including data loading, feature extraction, dictionary formation, VLAD encoding, and classification.
  • denseSIFTNV.m: Extracts Dense SIFT features from the dataset.
  • DictionaryFormationNV.m: Forms a dictionary using the extracted features.
  • VLADNV.m: Encodes features using VLAD.
  • splitTheDatastore.m: Splits the image datastore into training and testing sets.
How to Run
To run this project:
  1. Ensure MATLAB is installed on your system.
  2. Clone this repository to your local machine.
  3. Place your dataset in a directory of your choice.
  4. Open MATLAB and navigate to the cloned project directory.
  5. Run the VLAD.m script to start the image classification pipeline.
run('VLAD.m')
License
This code is for teaching/research purposes only.
About
This project implements an image classification pipeline using Vector of Locally Aggregated Descriptors.
Topics
computer-visionmatlabvlad
Resources
Readme
Activity
Stars
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Watchers
1 watching
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0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
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14 Commits
Folders and files
NameName
Last commit message
Last commit date
.vscode
.vscode
images
images
scene_categories
scene_categories
.gitattributes
.gitattributes
.gitignore
.gitignore
DictionaryFormationNV.m
DictionaryFormationNV.m
ProductQuantizationNV.m
ProductQuantizationNV.m
ProductQuantizerNV.asv
ProductQuantizerNV.asv
ProductQuantizerNV.m
ProductQuantizerNV.m
README.md
README.md
SIFTnormalizationNV.m
SIFTnormalizationNV.m
SplittingPhase.m
SplittingPhase.m
VLAD.m
VLAD.m
VLADNV.m
VLADNV.m
VLAD_plotting.m
VLAD_plotting.m
cvpr2010_jegou_douze_v1845.tar.gz
cvpr2010_jegou_douze_v1845.tar.gz
denseSIFTNV.asv
denseSIFTNV.asv
denseSIFTNV.m
denseSIFTNV.m
gaussNV.m
gaussNV.m
inria_fisher_v1.tgz
inria_fisher_v1.tgz
miniBatchKMeansNV.m
miniBatchKMeansNV.m
mustBePowerOfTwo.asv
mustBePowerOfTwo.asv
mustBePowerOfTwo.m
mustBePowerOfTwo.m
splitTheDatastore.m
splitTheDatastore.m
summaryTable.csv
summaryTable.csv
yael-master.zip
yael-master.zip
Repository files navigation
Image Classification using VLAD in MATLAB
This project implements an image classification pipeline using Vector of Locally Aggregated Descriptors (VLAD).
The pipeline involves extracting Dense SIFT features, forming a dictionary, encoding features using VLAD, and training an SVM classifier with various hyperparameter optimizations.
The goal is to achieve high accuracy in image classification by leveraging the VLAD encoding scheme.
Εικόνα8
Enlarged view of a Voronoi cell with local descriptors and the cell center.
Dotted lines depict the residuals between the local descriptors and the respective center.
Project Structure
  • VLAD.m: Main script to run the project, including data loading, feature extraction, dictionary formation, VLAD encoding, and classification.
  • denseSIFTNV.m: Extracts Dense SIFT features from the dataset.
  • DictionaryFormationNV.m: Forms a dictionary using the extracted features.
  • VLADNV.m: Encodes features using VLAD.
  • splitTheDatastore.m: Splits the image datastore into training and testing sets.
How to Run
To run this project:
  1. Ensure MATLAB is installed on your system.
  2. Clone this repository to your local machine.
  3. Place your dataset in a directory of your choice.
  4. Open MATLAB and navigate to the cloned project directory.
  5. Run the VLAD.m script to start the image classification pipeline.
run('VLAD.m')
License
This code is for teaching/research purposes only.
About
This project implements an image classification pipeline using Vector of Locally Aggregated Descriptors.
Topics
computer-visionmatlabvlad
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages