Comparative survey of visual object classifiers

TL;DR

This paper provides a comparative survey of various feature descriptors and classifiers used in visual object classification, highlighting their differences and helping to select optimal combinations for improved accuracy.

Contribution

It offers a comprehensive comparison of feature descriptors and classifiers, including SIFT, HeuSIFT, color descriptors, SVC, KNN, ADABOOST, and Fisher, aiding in method selection.

Findings

SIFT and HeuSIFT are effective feature descriptors.

Support Vector Classifier and ADABOOST show strong classification performance.

Combining multiple descriptors can enhance object classification accuracy.

Abstract

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.