TY - EJOU AU - Duan, Rong AU - Tan, Junshan AU - Qin, Jiaohua AU - Xiang, Xuyu AU - Tan, Yun AU - Xiong, Neal N. TI - Multi-Index Image Retrieval Hash Algorithm Based on Multi-View Feature Coding T2 - Computers, Materials \& Continua PY - 2020 VL - 65 IS - 3 SN - 1546-2226 AB - In recent years, with the massive growth of image data, how to match the image required by users quickly and efficiently becomes a challenge. Compared with single-view feature, multi-view feature is more accurate to describe image information. The advantages of hash method in reducing data storage and improving efficiency also make us study how to effectively apply to large-scale image retrieval. In this paper, a hash algorithm of multi-index image retrieval based on multi-view feature coding is proposed. By learning the data correlation between different views, this algorithm uses multi-view data with deeper level image semantics to achieve better retrieval results. This algorithm uses a quantitative hash method to generate binary sequences, and uses the hash code generated by the association features to construct database inverted index files, so as to reduce the memory burden and promote the efficient matching. In order to reduce the matching error of hash code and ensure the retrieval accuracy, this algorithm uses inverted multi-index structure instead of single-index structure. Compared with other advanced image retrieval method, this method has better retrieval performance. KW - Hashing KW - multi-view feature KW - large-scale image retrieval KW - feature coding KW - feature matching DO - 10.32604/cmc.2020.012161