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Statistical Approach to Texture Classification from Single Images

This repos provides an implementation for the "Statistical Approach to Texture Classification from Single Images" paper by Varma et. al.

The filters (RFS, LM, S) used in this repos are from this link

It is not documented yet. Since I know that it will take me some time to write the documentation, I decided to provide this initial version of the code. There is a lot of work that can help make this code better. So any contributions will be welcomed.

Libraries

To be able to run this code, you need to download the following libraries

VLFeat Library is used to calculate K-means (vl_kmeans) and the distance between new nodes and pre-computed centroids (vl_alldist). Classification toolbox is used to find the nearest neighbor during the classification phase.

Setup

  1. Download the code.
  2. Download the [Classification toolbox for
  3. MATLAB, by Milano Chemometrics and QSAR Research Group](http://michem.disat.unimib.it/chm/download/softwares/help_classification/web.htm).
  4. Update the knn_calc_dist.m file with the file inside this repos, to support chi-square distance
  5. Update the "rootpath" variable in demo_curet.m to point to Columbia-Utrecht dataset folder on your machine.
  6. Run demo_curet.m to test the performance over Columbia-Utrecht dataset.

I will try to update the documentation incrementally to provide more instructions to make using this code easier.

Contributor list

  1. Ahmed Taha
  2. Aleksandrs Ecins

License

TextureClassification_FilterBank is released under the BSD 2-Clause license. The code is released for unrestricted use.

About

This repos provides an MATLAB code implementation for the Statistical Approach to Texture Classification from Single Images paper by Varma et. al.

Topics

classification-toolboxcolumbia-utrecht-datasetmatlabtexture-classificationvarma

Resources

Readme
BSD-2-Clause license
Activity

Stars

11 stars

Watchers

1 watching

Forks

6 forks
Report repository

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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}
} 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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Statistical Approach to Texture Classification from Single Images
This repos provides an implementation for the "Statistical Approach to Texture Classification from Single Images" paper by Varma et. al.
The filters (RFS, LM, S) used in this repos are from this link
It is not documented yet. Since I know that it will take me some time to write the documentation, I decided to provide this initial version of the code. There is a lot of work that can help make this code better. So any contributions will be welcomed.
Libraries
To be able to run this code, you need to download the following libraries
VLFeat Library is used to calculate K-means (vl_kmeans) and the distance between new nodes and pre-computed centroids (vl_alldist).
Classification toolbox is used to find the nearest neighbor during the classification phase.
Setup
  1. Download the code.
  2. Download the [Classification toolbox for
  3. MATLAB, by Milano Chemometrics and QSAR Research Group](http://michem.disat.unimib.it/chm/download/softwares/help_classification/web.htm).
  4. Update the knn_calc_dist.m file with the file inside this repos, to support chi-square distance
  5. Update the "rootpath" variable in demo_curet.m to point to Columbia-Utrecht dataset folder on your machine.
  6. Run demo_curet.m to test the performance over Columbia-Utrecht dataset.
I will try to update the documentation incrementally to provide more instructions to make using this code easier.
Contributor list
  1. Ahmed Taha
  2. Aleksandrs Ecins
License
TextureClassification_FilterBank is released under the BSD 2-Clause license. The code is released for unrestricted use.
About
This repos provides an MATLAB code implementation for the Statistical Approach to Texture Classification from Single Images paper by Varma et. al.
Topics
classification-toolboxcolumbia-utrecht-datasetmatlabtexture-classificationvarma
Resources
Readme
BSD-2-Clause license
Activity
Stars
11 stars
Watchers
1 watching
Forks
6 forks
Report repository
Releases
Packages
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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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Statistical Approach to Texture Classification from Single Images
This repos provides an implementation for the "Statistical Approach to Texture Classification from Single Images" paper by Varma et. al.
The filters (RFS, LM, S) used in this repos are from this link
It is not documented yet. Since I know that it will take me some time to write the documentation, I decided to provide this initial version of the code. There is a lot of work that can help make this code better. So any contributions will be welcomed.
Libraries
To be able to run this code, you need to download the following libraries
VLFeat Library is used to calculate K-means (vl_kmeans) and the distance between new nodes and pre-computed centroids (vl_alldist).
Classification toolbox is used to find the nearest neighbor during the classification phase.
Setup
  1. Download the code.
  2. Download the [Classification toolbox for
  3. MATLAB, by Milano Chemometrics and QSAR Research Group](http://michem.disat.unimib.it/chm/download/softwares/help_classification/web.htm).
  4. Update the knn_calc_dist.m file with the file inside this repos, to support chi-square distance
  5. Update the "rootpath" variable in demo_curet.m to point to Columbia-Utrecht dataset folder on your machine.
  6. Run demo_curet.m to test the performance over Columbia-Utrecht dataset.
I will try to update the documentation incrementally to provide more instructions to make using this code easier.
Contributor list
  1. Ahmed Taha
  2. Aleksandrs Ecins
License
TextureClassification_FilterBank is released under the BSD 2-Clause license. The code is released for unrestricted use.
About
This repos provides an MATLAB code implementation for the Statistical Approach to Texture Classification from Single Images paper by Varma et. al.
Topics
classification-toolboxcolumbia-utrecht-datasetmatlabtexture-classificationvarma
Resources
Readme
BSD-2-Clause license
Activity
Stars
11 stars
Watchers
1 watching
Forks
6 forks
Report repository
Releases
Packages
Contributors
Languages

, '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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Statistical Approach to Texture Classification from Single Images
This repos provides an implementation for the "Statistical Approach to Texture Classification from Single Images" paper by Varma et. al.
The filters (RFS, LM, S) used in this repos are from this link
It is not documented yet. Since I know that it will take me some time to write the documentation, I decided to provide this initial version of the code. There is a lot of work that can help make this code better. So any contributions will be welcomed.
Libraries
To be able to run this code, you need to download the following libraries
VLFeat Library is used to calculate K-means (vl_kmeans) and the distance between new nodes and pre-computed centroids (vl_alldist).
Classification toolbox is used to find the nearest neighbor during the classification phase.
Setup
  1. Download the code.
  2. Download the [Classification toolbox for
  3. MATLAB, by Milano Chemometrics and QSAR Research Group](http://michem.disat.unimib.it/chm/download/softwares/help_classification/web.htm).
  4. Update the knn_calc_dist.m file with the file inside this repos, to support chi-square distance
  5. Update the "rootpath" variable in demo_curet.m to point to Columbia-Utrecht dataset folder on your machine.
  6. Run demo_curet.m to test the performance over Columbia-Utrecht dataset.
I will try to update the documentation incrementally to provide more instructions to make using this code easier.
Contributor list
  1. Ahmed Taha
  2. Aleksandrs Ecins
License
TextureClassification_FilterBank is released under the BSD 2-Clause license. The code is released for unrestricted use.
About
This repos provides an MATLAB code implementation for the Statistical Approach to Texture Classification from Single Images paper by Varma et. al.
Topics
classification-toolboxcolumbia-utrecht-datasetmatlabtexture-classificationvarma
Resources
Readme
BSD-2-Clause license
Activity
Stars
11 stars
Watchers
1 watching
Forks
6 forks
Report repository
Releases
Packages
Contributors
Languages

, '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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Statistical Approach to Texture Classification from Single Images
This repos provides an implementation for the "Statistical Approach to Texture Classification from Single Images" paper by Varma et. al.
The filters (RFS, LM, S) used in this repos are from this link
It is not documented yet. Since I know that it will take me some time to write the documentation, I decided to provide this initial version of the code. There is a lot of work that can help make this code better. So any contributions will be welcomed.
Libraries
To be able to run this code, you need to download the following libraries
VLFeat Library is used to calculate K-means (vl_kmeans) and the distance between new nodes and pre-computed centroids (vl_alldist).
Classification toolbox is used to find the nearest neighbor during the classification phase.
Setup
  1. Download the code.
  2. Download the [Classification toolbox for
  3. MATLAB, by Milano Chemometrics and QSAR Research Group](http://michem.disat.unimib.it/chm/download/softwares/help_classification/web.htm).
  4. Update the knn_calc_dist.m file with the file inside this repos, to support chi-square distance
  5. Update the "rootpath" variable in demo_curet.m to point to Columbia-Utrecht dataset folder on your machine.
  6. Run demo_curet.m to test the performance over Columbia-Utrecht dataset.
I will try to update the documentation incrementally to provide more instructions to make using this code easier.
Contributor list
  1. Ahmed Taha
  2. Aleksandrs Ecins
License
TextureClassification_FilterBank is released under the BSD 2-Clause license. The code is released for unrestricted use.
About
This repos provides an MATLAB code implementation for the Statistical Approach to Texture Classification from Single Images paper by Varma et. al.
Topics
classification-toolboxcolumbia-utrecht-datasetmatlabtexture-classificationvarma
Resources
Readme
BSD-2-Clause license
Activity
Stars
11 stars
Watchers
1 watching
Forks
6 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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Statistical Approach to Texture Classification from Single Images
This repos provides an implementation for the "Statistical Approach to Texture Classification from Single Images" paper by Varma et. al.
The filters (RFS, LM, S) used in this repos are from this link
It is not documented yet. Since I know that it will take me some time to write the documentation, I decided to provide this initial version of the code. There is a lot of work that can help make this code better. So any contributions will be welcomed.
Libraries
To be able to run this code, you need to download the following libraries
VLFeat Library is used to calculate K-means (vl_kmeans) and the distance between new nodes and pre-computed centroids (vl_alldist).
Classification toolbox is used to find the nearest neighbor during the classification phase.
Setup
  1. Download the code.
  2. Download the [Classification toolbox for
  3. MATLAB, by Milano Chemometrics and QSAR Research Group](http://michem.disat.unimib.it/chm/download/softwares/help_classification/web.htm).
  4. Update the knn_calc_dist.m file with the file inside this repos, to support chi-square distance
  5. Update the "rootpath" variable in demo_curet.m to point to Columbia-Utrecht dataset folder on your machine.
  6. Run demo_curet.m to test the performance over Columbia-Utrecht dataset.
I will try to update the documentation incrementally to provide more instructions to make using this code easier.
Contributor list
  1. Ahmed Taha
  2. Aleksandrs Ecins
License
TextureClassification_FilterBank is released under the BSD 2-Clause license. The code is released for unrestricted use.
About
This repos provides an MATLAB code implementation for the Statistical Approach to Texture Classification from Single Images paper by Varma et. al.
Topics
classification-toolboxcolumbia-utrecht-datasetmatlabtexture-classificationvarma
Resources
Readme
BSD-2-Clause license
Activity
Stars
11 stars
Watchers
1 watching
Forks
6 forks
Report repository
Releases
Packages
Contributors
Languages

, '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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Statistical Approach to Texture Classification from Single Images
This repos provides an implementation for the "Statistical Approach to Texture Classification from Single Images" paper by Varma et. al.
The filters (RFS, LM, S) used in this repos are from this link
It is not documented yet. Since I know that it will take me some time to write the documentation, I decided to provide this initial version of the code. There is a lot of work that can help make this code better. So any contributions will be welcomed.
Libraries
To be able to run this code, you need to download the following libraries
VLFeat Library is used to calculate K-means (vl_kmeans) and the distance between new nodes and pre-computed centroids (vl_alldist).
Classification toolbox is used to find the nearest neighbor during the classification phase.
Setup
  1. Download the code.
  2. Download the [Classification toolbox for
  3. MATLAB, by Milano Chemometrics and QSAR Research Group](http://michem.disat.unimib.it/chm/download/softwares/help_classification/web.htm).
  4. Update the knn_calc_dist.m file with the file inside this repos, to support chi-square distance
  5. Update the "rootpath" variable in demo_curet.m to point to Columbia-Utrecht dataset folder on your machine.
  6. Run demo_curet.m to test the performance over Columbia-Utrecht dataset.
I will try to update the documentation incrementally to provide more instructions to make using this code easier.
Contributor list
  1. Ahmed Taha
  2. Aleksandrs Ecins
License
TextureClassification_FilterBank is released under the BSD 2-Clause license. The code is released for unrestricted use.
About
This repos provides an MATLAB code implementation for the Statistical Approach to Texture Classification from Single Images paper by Varma et. al.
Topics
classification-toolboxcolumbia-utrecht-datasetmatlabtexture-classificationvarma
Resources
Readme
BSD-2-Clause license
Activity
Stars
11 stars
Watchers
1 watching
Forks
6 forks
Report repository
Releases
Packages
Contributors
Languages

, '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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Statistical Approach to Texture Classification from Single Images
This repos provides an implementation for the "Statistical Approach to Texture Classification from Single Images" paper by Varma et. al.
The filters (RFS, LM, S) used in this repos are from this link
It is not documented yet. Since I know that it will take me some time to write the documentation, I decided to provide this initial version of the code. There is a lot of work that can help make this code better. So any contributions will be welcomed.
Libraries
To be able to run this code, you need to download the following libraries
VLFeat Library is used to calculate K-means (vl_kmeans) and the distance between new nodes and pre-computed centroids (vl_alldist).
Classification toolbox is used to find the nearest neighbor during the classification phase.
Setup
  1. Download the code.
  2. Download the [Classification toolbox for
  3. MATLAB, by Milano Chemometrics and QSAR Research Group](http://michem.disat.unimib.it/chm/download/softwares/help_classification/web.htm).
  4. Update the knn_calc_dist.m file with the file inside this repos, to support chi-square distance
  5. Update the "rootpath" variable in demo_curet.m to point to Columbia-Utrecht dataset folder on your machine.
  6. Run demo_curet.m to test the performance over Columbia-Utrecht dataset.
I will try to update the documentation incrementally to provide more instructions to make using this code easier.
Contributor list
  1. Ahmed Taha
  2. Aleksandrs Ecins
License
TextureClassification_FilterBank is released under the BSD 2-Clause license. The code is released for unrestricted use.
About
This repos provides an MATLAB code implementation for the Statistical Approach to Texture Classification from Single Images paper by Varma et. al.
Topics
classification-toolboxcolumbia-utrecht-datasetmatlabtexture-classificationvarma
Resources
Readme
BSD-2-Clause license
Activity
Stars
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