Benchmark of multiple approaches for feature extraction and image similarity characterization

Abstract

The performance of image classification largely depends on both the discrimination power of the visual features for image content representation and the effectiveness of the kernels for diverse image similarity characterization. Different types of kernels have been developed for SVM image classifier training, and different research teams may use different types of visual features in their experiments. Thus there is an urgent need to provide benchmark work to assess the real performance of different types of visual features and kernels for various image classification tasks. In this paper, we have benchmarked multiple approaches for feature extraction and image similarity characterization, so that some useful guidelines can be provided for: (a) how to select more effective approach for feature extraction and enhance the discrimination power of various types of visual features; and (b) how to combine multiple types of visual features and their kernels to enhance the discrimination power of SVM image classifiers. Our experiments on large-scale image collections have also obtained very positive results.