Collaborative hashing adopted in locality-constrained linear coding for scene classification

作     者：Zhou, Zhiping Li, Chunye Zhao, Xiaoxiao Zhou, Fangzheng 

作者机构：Jiangnan Univ Sch Internet Things Engn Wuxi 214122 Jiangsu Peoples R China Jiangnan Univ Engn Res Ctr Internet Things Technol Applicat Minist Educ Wuxi 214122 Jiangsu Peoples R China Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China 

出 版 物：《MULTIMEDIA TOOLS AND APPLICATIONS》 (多媒体工具和应用)

年 卷 期：2019年第78卷第12期

页      面：16329-16343页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Center of Internet of Things Technology Applications Ministry of Education Jiangnan University 

主　　题：Approximate nearest neighbor Locality-constrained linear coding Collaborative hashing 

摘      要：Scene classification methods based on effective feature extraction and coding have obtained promising results in recent years. But the K-nearest neighbor search strategy in Locality-constrained Linear Coding (LLC) increases the complexity of the algorithm due to the exhaustive search. To solve the problem, an improved approximate nearest neighbor search strategy is proposed to improve the computational efficiency of LLC. Considering the mapping relationship between the visual words and features, a collaborative hashing method is incorporated to transform the high dimensional features into binary code form, and the original Euclidean space is transformed into the Hamming space that consists of multi similar features. The similar visual words can be queried quickly. Then the nearest neighbors can be searched efficiently through Hamming distance ranking, which can improve the coding efficiency. The experimental results on standard datasets demonstrate the effectiveness of the proposed approach, and the average classification accuracy can be improved.