  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.
