New Approach to Improve the Classification Process of Multi-Class Objects

Abstract

In recent years, several descriptors have been proposed in many image classification applications. Accelerated-KAZE (A-KAZE) is considered one of the descriptors that has shown high performance for feature extraction. A-KAZE uses a binary descriptor called modified-local difference binary, which is very efficient and invariant to changes in rotation and scale. This representation does not allow spatial information to be considered between objects in the image, which makes it possible to reduce the performances of the classification of the images. This article broaches a new approach to improve the performance of the A-KAZE descriptor for image classification. The authors first establish the connection between the A-KAZE descriptor and the bag of feature model. Then the Spatial Pyramid Matching (SPM) is adopted by exploiting the A-KAZE descriptor to reinforce its robustness by introducing spatial information. The results of the experiments on several datasets show that the A-KAZE descriptor with SPM gives very satisfactory results compared with other existing methods in the state of the art.