关键词: 区分性图像特征, 主元分析, 特征聚类, 正交旋转矩阵, 哈希函数, 多表索引

Abstract: In the era of big data, fast and accurate indexing algorithm is crucial for the information retrieval. As the search performance of Single Table Hash Retrieval（STHR） method utilizing random projection is too low, a novel multi table image hash retrieval method based on principal component is proposed. In order to get efficient hash encoding to distinguish the feature of different semantic samples, discriminative image features are holden by the principal component analysis method. Furthermore, hash index tables are established by using Feature Clustering as a guide for learning hash projection. Meaning while, the orthogonal rotation matrix is employed to optimize the hash projection, which ensures that the samples with the same semantics have similar hash codes. Finally, a series of experiments are conducted on CIFAR-10 and Caltech-256 data sets comparing with the related methods. The results show that the proposed method gains a great improvement in terms of retrieval performance.

Key words: distinguishing feature of image, Principal Component Analysis（PCA）, feature clustering, orthogonal rotation matrix, hash function, multi table index