A semantic region descriptor for local feature based image categorization
Region descriptor has proved to be very important for local feature based image categorization. Previous region descriptors are usually based on the statistics of low level features, such as intensity, edge response, and etc. In this paper a novel descriptor named local texton statistics (LTS) that explores the high level semantic statistical characteristics of image regions is presented. Perceptual information is obtained by applying Gaussian filter banks and the image regions are described by the statistics of different 'texton's. Using the bag of words as classification algorithm, experiments show that the proposed descriptor is superior to the previous popular SIFT descriptors on the Wang dataset. The combination of these two descriptors shows high performance for categorization on both the Wang dataset and the fifteen scene categories dataset.