Scenes/Objects classification toolbox

Scenes recognition toolbox for vision systems

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