Comparative survey of visual object classifiers
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Classification of Visual Object Classes represents one of the most elaborated areas of interest in Computer Vision. It is always challenging to get one specific detector, descriptor or classifier that provides the expected object classification result. Consequently, it critical to compare the different detection, descriptor and classifier methods available and chose a single or combination of two or three to get an optimal result. In this paper, we have presented a comparative survey of different feature descriptors and classifiers. From feature descriptors, SIFT (Sparse & Dense) and HeuSIFT combination colour descriptors; From classification techniques, Support Vector Classifier, K-Nearest Neighbor, ADABOOST, and fisher are covered in comparative practical implementation survey.
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