Scenes/Objects classification toolbox

Scenes recognition toolbox for vision systems
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Updated 25 Apr 2011

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Scenes/Objects Rocognition toolbox v0.12
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This toolbox provides some basic tools for scenes/object recognition in vision systems.
Based on supervised classification, this toolbox offerts some state-of-art descriptors coupled with fast and efficient classifiers.
Descriptors are divided in two famillies:
i) "direct" features computed from images [1,2,3,4,5,19],
ii) "dictionnary learning + spatial pooling" features computed from a collection of patches:
a) Bag of Features [6,7] and
b) Sparse Dictionary learning [8,9].
Large-Scale Linear SVM such Liblinear [10] or Pagasos [11] are used to train models since features are almost perfectly linearly separable.
Non-linear Kernels extension for additive homogeneous kernels (chi2, intersection histogram, etc...) is performed through features map method [12,22].
The main objective of this toolbox is to deliver simple but efficient tools, easy to modify, mainly written in C codes with a matlab interface.

Please open readme.txt for full instructions

Cite As

Sebastien PARIS (2024). Scenes/Objects classification toolbox (https://www.mathworks.com/matlabcentral/fileexchange/29800-scenes-objects-classification-toolbox), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2009b
Compatible with any release
Platform Compatibility
Windows macOS Linux
Categories
Find more on Recognition, Object Detection, and Semantic Segmentation in Help Center and MATLAB Answers

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Version Published Release Notes
1.2.0.0

0.12 - Add new normalization method.
- Better default parameters which improve results.
- Better databases handling.
- Add "triangle" soft assignement for K-means dictionary learning approach [21]

1.1.0.0

0.11
-Compile SPAMS with -largeArrayDims flag for WIN64 system
-Can handle any many databases where images per topics are in a specific folder with specific config files
- Read readme.txt for a complete of changes

1.0.0.0