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Image Retrieval using Multi-Texton Histogram

Implementation of Content Based Image Retrieval Process based on Multi-Texton Histogram described by Guang-Hai Liu et al. in the paper using Python

Multi-Texton Histogram

MTH is a generalized visual attribute descriptor but without any image segmentation or model training and is based on Julesz’s textons theory. It can be used as both shape as well as color descriptor. The algorithm analyzes the spatial correlation between neighboring color and edge orientation based on four special texton types (depicted below), and then creates the texton co-occurrence matrix to describe the attributes using histogram.

Algorithm

  • Image is split into Red, Blue, Green color channels
  • Sobel Operator is applied to each of the channels (RGB color channel)
  • Color Quantization in RGB color space
  • Texton Detection: the figure below describes the textons that are to be detected Texton

Texton Detection Process

texton detection

Repository Structure

  • 226.jpg and 2712.jpg: Images from Corel-1k dataset used for visualization of the histogram in mth_retrieval.ipynb and Multi_Texton.ipynb respectively
  • Multi_Texton.ipynb: Jupyter Notebook used for explaining code for extracting features using multi-texton of an input image
  • mth_retrieval.ipynb: Jupyter Notebook used for explaining code for retrieval of image from the MongoDB database.
  • MTH.py : Python code responsible for extracting the features from the images and seeding it into MongoDB database.
  • retrieval.py : Python code responsible for retrieving the similar images from the database.
  • dump/MTH: Folder containing the actual dump (82 bin feature-vector for each image) of the seeded images in the database. It can be restored as:
cd Directory
mongorestore --db db_name .

Dataset used

Dataset used for the project is Corel-1k dataset which contains 1000 images from diverse contents such as building, horses, people, elephants, mountains, etc. Each image is of size 192×128 or 128×192 in the JPEG format. The dataset can be downloaded from Corel-1K

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Content Based Image Retrieval based on Multi-Texton Histogram

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Image Retrieval using Multi-Texton Histogram
Implementation of Content Based Image Retrieval Process based on Multi-Texton Histogram described by Guang-Hai Liu et al. in the paper using Python
Multi-Texton Histogram
MTH is a generalized visual attribute descriptor but without any image segmentation or model training and is based on Julesz’s textons theory.
It can be used as both shape as well as color descriptor. The algorithm analyzes the spatial correlation between neighboring color and edge orientation based on four special texton types (depicted below), and then creates the texton co-occurrence matrix to describe the attributes using histogram.
Algorithm
  • Image is split into Red, Blue, Green color channels
  • Sobel Operator is applied to each of the channels (RGB color channel)
  • Color Quantization in RGB color space
  • Texton Detection: the figure below describes the textons that are to be detected
Texton
Texton Detection Process
texton detection
Repository Structure
  • 226.jpg and 2712.jpg: Images from Corel-1k dataset used for visualization of the histogram in mth_retrieval.ipynb and Multi_Texton.ipynb respectively
  • Multi_Texton.ipynb: Jupyter Notebook used for explaining code for extracting features using multi-texton of an input image
  • mth_retrieval.ipynb: Jupyter Notebook used for explaining code for retrieval of image from the MongoDB database.
  • MTH.py : Python code responsible for extracting the features from the images and seeding it into MongoDB database.
  • retrieval.py : Python code responsible for retrieving the similar images from the database.
  • dump/MTH: Folder containing the actual dump (82 bin feature-vector for each image) of the seeded images in the database. It can be restored as:
cd Directory
mongorestore --db db_name .

Dataset used
Dataset used for the project is Corel-1k dataset which contains 1000 images from diverse contents such as building, horses, people, elephants, mountains, etc. Each image is of size 192×128 or 128×192 in the JPEG format. The dataset can be downloaded from Corel-1K
About
Content Based Image Retrieval based on Multi-Texton Histogram
Topics
cbircontent-based-image-retrievalimage-retrievalmthmth-in-pythonmulti-texton-histogram
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Readme
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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");
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})();
(function(){
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Image Retrieval using Multi-Texton Histogram
Implementation of Content Based Image Retrieval Process based on Multi-Texton Histogram described by Guang-Hai Liu et al. in the paper using Python
Multi-Texton Histogram
MTH is a generalized visual attribute descriptor but without any image segmentation or model training and is based on Julesz’s textons theory.
It can be used as both shape as well as color descriptor. The algorithm analyzes the spatial correlation between neighboring color and edge orientation based on four special texton types (depicted below), and then creates the texton co-occurrence matrix to describe the attributes using histogram.
Algorithm
  • Image is split into Red, Blue, Green color channels
  • Sobel Operator is applied to each of the channels (RGB color channel)
  • Color Quantization in RGB color space
  • Texton Detection: the figure below describes the textons that are to be detected
Texton
Texton Detection Process
texton detection
Repository Structure
  • 226.jpg and 2712.jpg: Images from Corel-1k dataset used for visualization of the histogram in mth_retrieval.ipynb and Multi_Texton.ipynb respectively
  • Multi_Texton.ipynb: Jupyter Notebook used for explaining code for extracting features using multi-texton of an input image
  • mth_retrieval.ipynb: Jupyter Notebook used for explaining code for retrieval of image from the MongoDB database.
  • MTH.py : Python code responsible for extracting the features from the images and seeding it into MongoDB database.
  • retrieval.py : Python code responsible for retrieving the similar images from the database.
  • dump/MTH: Folder containing the actual dump (82 bin feature-vector for each image) of the seeded images in the database. It can be restored as:
cd Directory
mongorestore --db db_name .

Dataset used
Dataset used for the project is Corel-1k dataset which contains 1000 images from diverse contents such as building, horses, people, elephants, mountains, etc. Each image is of size 192×128 or 128×192 in the JPEG format. The dataset can be downloaded from Corel-1K
About
Content Based Image Retrieval based on Multi-Texton Histogram
Topics
cbircontent-based-image-retrievalimage-retrievalmthmth-in-pythonmulti-texton-histogram
Resources
Readme
Activity
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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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BranchesTags
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Image Retrieval using Multi-Texton Histogram
Implementation of Content Based Image Retrieval Process based on Multi-Texton Histogram described by Guang-Hai Liu et al. in the paper using Python
Multi-Texton Histogram
MTH is a generalized visual attribute descriptor but without any image segmentation or model training and is based on Julesz’s textons theory.
It can be used as both shape as well as color descriptor. The algorithm analyzes the spatial correlation between neighboring color and edge orientation based on four special texton types (depicted below), and then creates the texton co-occurrence matrix to describe the attributes using histogram.
Algorithm
  • Image is split into Red, Blue, Green color channels
  • Sobel Operator is applied to each of the channels (RGB color channel)
  • Color Quantization in RGB color space
  • Texton Detection: the figure below describes the textons that are to be detected
Texton
Texton Detection Process
texton detection
Repository Structure
  • 226.jpg and 2712.jpg: Images from Corel-1k dataset used for visualization of the histogram in mth_retrieval.ipynb and Multi_Texton.ipynb respectively
  • Multi_Texton.ipynb: Jupyter Notebook used for explaining code for extracting features using multi-texton of an input image
  • mth_retrieval.ipynb: Jupyter Notebook used for explaining code for retrieval of image from the MongoDB database.
  • MTH.py : Python code responsible for extracting the features from the images and seeding it into MongoDB database.
  • retrieval.py : Python code responsible for retrieving the similar images from the database.
  • dump/MTH: Folder containing the actual dump (82 bin feature-vector for each image) of the seeded images in the database. It can be restored as:
cd Directory
mongorestore --db db_name .

Dataset used
Dataset used for the project is Corel-1k dataset which contains 1000 images from diverse contents such as building, horses, people, elephants, mountains, etc. Each image is of size 192×128 or 128×192 in the JPEG format. The dataset can be downloaded from Corel-1K
About
Content Based Image Retrieval based on Multi-Texton Histogram
Topics
cbircontent-based-image-retrievalimage-retrievalmthmth-in-pythonmulti-texton-histogram
Resources
Readme
Activity
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1 watching
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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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BranchesTags
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Image Retrieval using Multi-Texton Histogram
Implementation of Content Based Image Retrieval Process based on Multi-Texton Histogram described by Guang-Hai Liu et al. in the paper using Python
Multi-Texton Histogram
MTH is a generalized visual attribute descriptor but without any image segmentation or model training and is based on Julesz’s textons theory.
It can be used as both shape as well as color descriptor. The algorithm analyzes the spatial correlation between neighboring color and edge orientation based on four special texton types (depicted below), and then creates the texton co-occurrence matrix to describe the attributes using histogram.
Algorithm
  • Image is split into Red, Blue, Green color channels
  • Sobel Operator is applied to each of the channels (RGB color channel)
  • Color Quantization in RGB color space
  • Texton Detection: the figure below describes the textons that are to be detected
Texton
Texton Detection Process
texton detection
Repository Structure
  • 226.jpg and 2712.jpg: Images from Corel-1k dataset used for visualization of the histogram in mth_retrieval.ipynb and Multi_Texton.ipynb respectively
  • Multi_Texton.ipynb: Jupyter Notebook used for explaining code for extracting features using multi-texton of an input image
  • mth_retrieval.ipynb: Jupyter Notebook used for explaining code for retrieval of image from the MongoDB database.
  • MTH.py : Python code responsible for extracting the features from the images and seeding it into MongoDB database.
  • retrieval.py : Python code responsible for retrieving the similar images from the database.
  • dump/MTH: Folder containing the actual dump (82 bin feature-vector for each image) of the seeded images in the database. It can be restored as:
cd Directory
mongorestore --db db_name .

Dataset used
Dataset used for the project is Corel-1k dataset which contains 1000 images from diverse contents such as building, horses, people, elephants, mountains, etc. Each image is of size 192×128 or 128×192 in the JPEG format. The dataset can be downloaded from Corel-1K
About
Content Based Image Retrieval based on Multi-Texton Histogram
Topics
cbircontent-based-image-retrievalimage-retrievalmthmth-in-pythonmulti-texton-histogram
Resources
Readme
Activity
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Watchers
1 watching
Forks
3 forks
Report repository
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, '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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Image Retrieval using Multi-Texton Histogram
Implementation of Content Based Image Retrieval Process based on Multi-Texton Histogram described by Guang-Hai Liu et al. in the paper using Python
Multi-Texton Histogram
MTH is a generalized visual attribute descriptor but without any image segmentation or model training and is based on Julesz’s textons theory.
It can be used as both shape as well as color descriptor. The algorithm analyzes the spatial correlation between neighboring color and edge orientation based on four special texton types (depicted below), and then creates the texton co-occurrence matrix to describe the attributes using histogram.
Algorithm
  • Image is split into Red, Blue, Green color channels
  • Sobel Operator is applied to each of the channels (RGB color channel)
  • Color Quantization in RGB color space
  • Texton Detection: the figure below describes the textons that are to be detected
Texton
Texton Detection Process
texton detection
Repository Structure
  • 226.jpg and 2712.jpg: Images from Corel-1k dataset used for visualization of the histogram in mth_retrieval.ipynb and Multi_Texton.ipynb respectively
  • Multi_Texton.ipynb: Jupyter Notebook used for explaining code for extracting features using multi-texton of an input image
  • mth_retrieval.ipynb: Jupyter Notebook used for explaining code for retrieval of image from the MongoDB database.
  • MTH.py : Python code responsible for extracting the features from the images and seeding it into MongoDB database.
  • retrieval.py : Python code responsible for retrieving the similar images from the database.
  • dump/MTH: Folder containing the actual dump (82 bin feature-vector for each image) of the seeded images in the database. It can be restored as:
cd Directory
mongorestore --db db_name .

Dataset used
Dataset used for the project is Corel-1k dataset which contains 1000 images from diverse contents such as building, horses, people, elephants, mountains, etc. Each image is of size 192×128 or 128×192 in the JPEG format. The dataset can be downloaded from Corel-1K
About
Content Based Image Retrieval based on Multi-Texton Histogram
Topics
cbircontent-based-image-retrievalimage-retrievalmthmth-in-pythonmulti-texton-histogram
Resources
Readme
Activity
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1 watching
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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");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Image Retrieval using Multi-Texton Histogram
Implementation of Content Based Image Retrieval Process based on Multi-Texton Histogram described by Guang-Hai Liu et al. in the paper using Python
Multi-Texton Histogram
MTH is a generalized visual attribute descriptor but without any image segmentation or model training and is based on Julesz’s textons theory.
It can be used as both shape as well as color descriptor. The algorithm analyzes the spatial correlation between neighboring color and edge orientation based on four special texton types (depicted below), and then creates the texton co-occurrence matrix to describe the attributes using histogram.
Algorithm
  • Image is split into Red, Blue, Green color channels
  • Sobel Operator is applied to each of the channels (RGB color channel)
  • Color Quantization in RGB color space
  • Texton Detection: the figure below describes the textons that are to be detected
Texton
Texton Detection Process
texton detection
Repository Structure
  • 226.jpg and 2712.jpg: Images from Corel-1k dataset used for visualization of the histogram in mth_retrieval.ipynb and Multi_Texton.ipynb respectively
  • Multi_Texton.ipynb: Jupyter Notebook used for explaining code for extracting features using multi-texton of an input image
  • mth_retrieval.ipynb: Jupyter Notebook used for explaining code for retrieval of image from the MongoDB database.
  • MTH.py : Python code responsible for extracting the features from the images and seeding it into MongoDB database.
  • retrieval.py : Python code responsible for retrieving the similar images from the database.
  • dump/MTH: Folder containing the actual dump (82 bin feature-vector for each image) of the seeded images in the database. It can be restored as:
cd Directory
mongorestore --db db_name .

Dataset used
Dataset used for the project is Corel-1k dataset which contains 1000 images from diverse contents such as building, horses, people, elephants, mountains, etc. Each image is of size 192×128 or 128×192 in the JPEG format. The dataset can be downloaded from Corel-1K
About
Content Based Image Retrieval based on Multi-Texton Histogram
Topics
cbircontent-based-image-retrievalimage-retrievalmthmth-in-pythonmulti-texton-histogram
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Readme
Activity
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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); }
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Image Retrieval using Multi-Texton Histogram
Implementation of Content Based Image Retrieval Process based on Multi-Texton Histogram described by Guang-Hai Liu et al. in the paper using Python
Multi-Texton Histogram
MTH is a generalized visual attribute descriptor but without any image segmentation or model training and is based on Julesz’s textons theory.
It can be used as both shape as well as color descriptor. The algorithm analyzes the spatial correlation between neighboring color and edge orientation based on four special texton types (depicted below), and then creates the texton co-occurrence matrix to describe the attributes using histogram.
Algorithm
  • Image is split into Red, Blue, Green color channels
  • Sobel Operator is applied to each of the channels (RGB color channel)
  • Color Quantization in RGB color space
  • Texton Detection: the figure below describes the textons that are to be detected
Texton
Texton Detection Process
texton detection
Repository Structure
  • 226.jpg and 2712.jpg: Images from Corel-1k dataset used for visualization of the histogram in mth_retrieval.ipynb and Multi_Texton.ipynb respectively
  • Multi_Texton.ipynb: Jupyter Notebook used for explaining code for extracting features using multi-texton of an input image
  • mth_retrieval.ipynb: Jupyter Notebook used for explaining code for retrieval of image from the MongoDB database.
  • MTH.py : Python code responsible for extracting the features from the images and seeding it into MongoDB database.
  • retrieval.py : Python code responsible for retrieving the similar images from the database.
  • dump/MTH: Folder containing the actual dump (82 bin feature-vector for each image) of the seeded images in the database. It can be restored as:
cd Directory
mongorestore --db db_name .

Dataset used
Dataset used for the project is Corel-1k dataset which contains 1000 images from diverse contents such as building, horses, people, elephants, mountains, etc. Each image is of size 192×128 or 128×192 in the JPEG format. The dataset can be downloaded from Corel-1K
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Content Based Image Retrieval based on Multi-Texton Histogram
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cbircontent-based-image-retrievalimage-retrievalmthmth-in-pythonmulti-texton-histogram
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