Texture classification based on SIFT features and bag-of-words in compressed domain

This paper presents a new method for texture image classification based on compressed sensing (CS) and feature representation using scale-invariant Bag-of-Words model (SIBW). The issue is discussed from the following three aspects: 1) texture image compression, 2) Scale Invariant Feature Transform (SIFT) feature extraction in compressed domain and 3) classification by using SIBW. The texture classification method was tested on six common classes of texture images, the results show that the excellent performance can be achieved by the proposed approach, and classification accuracy has been greatly improved.