Semantic-aware Hashing for Social Image Retrieval
Jinhui Tang, Zechao Li, Liyan Zhang, Qingming Huang · 2015
With the proliferation of large-scale social images, recent years have witnessed the increasing amount of images with user-provided tags, which leads to considerable effort made on hashing based approximate nearest neighbor (ANN) search in huge databases. In this work, we propose a novel Semantic-aware Hashing method (SaH) by discovering knowledge from these social media resources to implement approximate similarity search. Different from the previous work, the proposed method learns semantic hashing codes by exploiting heterogeneous information from the textual and visual domains. The semantic structure in the textual domain is well preserved to learn the binary codes. To handle the noisy, incomplete, or subjective user-provided tags, the visual structure is also leveraged. On the other hand, an information theoretic regularization is exploited by using maximum entropy principle and a row-wise sparse model with l2,p (0 < p ≤ 1) mixed norm is introduced to filter certain noisy or redundant visual features. Experiments are conducted on a widely-used social image dataset and the comparison results demonstrate the outperforming performance of the proposed SaH method over state-of-the-art hashing techniques.