Object class recognition using combination of color SIFT descriptors

Abstract

Classifying the unknown image into the correct related class is the aim of the object class recognition systems. Two main points should be kept in mind to implement a class recognition system. Which descriptors that have a higher discriminative power that needs to be extracted from the images? Which classifier can classify these descriptors successfully? The most famous image descriptor is the Scale Invariant Feature Transform (SIFT). Although, SIFT has a high performance, it is partially an illumination invariant. Adding local color information to SIFT descriptors are then suggested to increase the illumination invariant, these descriptors can be called color SIFT descriptors. In this paper, different color SIFT descriptors were implemented to evaluate their performance in the object class recognition systems. This is due to the fact that some descriptors may have a good performance in one class and bad performance in another class at the same time. All possible combinations of these descriptors were used. Some combinations of color SIFT descriptors achieved remarkable classification accuracy. Non linear χ ² -kernel support vector machine is used as a learning classifier and bag-of-features representation is used to represent the image features in this paper.