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With benefits of low storage costs and high query speeds, hashing methods are widely researched for efficiently retrieving large-scale data, which commonly contains multiple views, e.g. a news report with images, videos and texts. In this paper, we study the problem of cross-view retrieval and propose an effective Semantics-Preserving Hashing method, termed(More)
Though widely utilized for facilitating image management, user-provided image tags are usually incomplete and insufficient to describe the whole semantic content of corresponding images, resulting in performance degradations in tag-dependent applications and thus necessitating effective tag completion methods. In this paper, we propose a novel scheme(More)
To tackle a multi-label classification problem with many classes, recently label space dimension reduction (LSDR) is proposed. It encodes the original label space to a low-dimensional latent space and uses a decoding process for recovery. In this paper, we propose a novel method termed FaIE to perform LSDR via Feature-aware Implicit label space Encoding.(More)
The quantity setting of visual neighbours can be critical for the performance of many previously proposed visual-neighbour-based (VNB) image auto-annotation methods. And in those methods, each candidate tag of a to-be-annotated image would be better to have its own trustworthy part of visual neighbours for score prediction. Hence in this paper we propose to(More)
User-provided textual tags of web images are widely utilized for facilitating image management and retrieval. Yet they are usually incomplete and insufficient to describe the whole semantic content of the corresponding images, resulting in performance degradations of various tag-dependent applications. In this paper, we propose a novel method denoted as(More)
In this paper, we propose a novel image auto-annotation model using tag-related random search over range-constrained visual neighbors of the to-be-annotated image. The proposed model, termed as TagSearcher, observes that the annotating performances of many previous visual-neighbor-based models are generally sensitive to the quantity setting of visual(More)
For efficiently retrieving nearest neighbors from large-scale multiview data, recently hashing methods are widely investigated, which can substantially improve query speeds. In this paper, we propose an effective probability-based semantics-preserving hashing (SePH) method to tackle the problem of cross-view retrieval. Considering the semantic consistency(More)
With the dramatic development of the Internet, how to exploit large-scale retrieval techniques for multimodal web data has become one of the most popular but challenging problems in computer vision and multimedia. Recently, hashing methods are used for fast nearest neighbor search in large-scale data spaces, by embedding high-dimensional feature descriptors(More)
Though the field of image auto-annotation has been extensively researched, most previous work concentrated on the single-source problem, assuming that both labelled and unseen to-be-annotated images are from a single source (e.g. an identical website), while in practice they are generally collected from multiple sources (e.g. different websites). In that(More)