Click-through-based cross-view learning for image search

• Main Authors: PAN, Yingwei, YAO, Ting, MEI, Tao, LI, Houqiang, NGO, Chong-wah, RUI, Yong
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2014
• Subjects: Clickthrough data; Cross-view learning; DNN image representation; Image search; Subspace learning; Data Storage Systems; Graphics and Human Computer Interfaces
• Online Access: https://ink.library.smu.edu.sg/sis_research/6514; https://ink.library.smu.edu.sg/context/sis_research/article/7517/viewcontent/2600428.2609568.pdf
• Institution: Singapore Management University

One of the fundamental problems in image search is to rank image documents according to a given textual query. Existing search engines highly depend on surrounding texts for ranking images, or leverage the query-image pairs annotated by human labelers to train a series of ranking functions. However, there are two major limitations: 1) the surrounding texts are often noisy or too few to accurately describe the image content, and 2) the human annotations are resourcefully expensive and thus cannot be scaled up. We demonstrate in this paper that the above two fundamental challenges can be mitigated by jointly exploring the cross-view learning and the use of click-through data. The former aims to create a latent subspace with the ability in comparing information from the original incomparable views (i.e., textual and visual views), while the latter explores the largely available and freely accessible click-through data (i.e., “crowdsourced” human intelligence) for understanding query. Specifically, we propose a novel cross-view learning method for image search, named Click-through-based Crossview Learning (CCL), by jointly minimizing the distance between the mappings of query and image in the latent subspace and preserving the inherent structure in each original space. On a large-scale click-based image dataset, CCL achieves the improvement over Support Vector Machinebased method by 4.0% in terms of relevance, while reducing the feature dimension by several orders of magnitude (e.g., from thousands to tens). Moreover, the experiments also demonstrate the superior performance of CCL to several state-of-the-art subspace learning techniques.