Locality constrained encoding of frequency and spatial information for image classification

The bag-of-feature (BoF) model provides a way to construct high-level representation for image classification. Although spatial pyramid matching (SPM) has been incorporated into many of its extensions, these models intrinsically lack the mechanism to utilize frequency domain information. In this paper, we propose the locality-constrained encoding of frequency and spatial information (LEFSI) algorithm, in which an image is decomposed into multiple frequency components and each component is further decomposed into subregions using SPM. The scale-invariant feature transform (SIFT) descriptors are first calculated in each subregion, and then converted into a global descriptor by using the codebook generated on a category-by-category basis and locality-constrained linear coding (LLC). The image feature is defined as the concatenation of global descriptors constructed in all subregions. We evaluated this algorithm against several state-of-the-art models on six benchmark datasets. Our results suggest that the proposed LEFSI algorithm can describe images more effectively and provide more accurate image classification.