Insect species recognition using discriminative local soft coding

An Lu, Xinwen Hou, Cheng-Lin Liu, Xiaolin Chen

Abstract: Insect species recognition is more difficult than generic object recognition because of the similarity between different species. In this paper, we propose a hybrid approach called discriminative local soft coding (DLSoft) which combines local and discriminative coding strategies together. Our method takes use of neighbor codewords to get a local soft coding and class specific codebooks (sets of codewords) for a discriminative representation. On obtaining the vector representation of image via spatial pyramid pooling of patches, a linear SVM classifier is used to classify images into species. Experimental results show that the proposed method performs well on insect species recognition and outperforms the state-of-the-art methods on generic object categorization.