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Cross-media retrieval has received increasing interest in recent years, which aims to addressing the semantic correlation issues within rich media. As two key aspects, cross-media representation and indexing have been studied for dealing with cross-media similarity measure and the scalability issue, respectively. In this paper, we propose a new cross-media hashing scheme, called Centroid Approaching...
Automatic image tagging (AIT) is an effective technology to facilitate the process of image retrieval without requiring user to provide a retrieval instance beforehand. In this paper, we propose an AIT method based on kernel canonical correlation analysis (KCCA) with similarity refinement (KCCSR). As a statistic correlation technique, the KCCA aims at extracting some kind of hidden information shared...
Automatic TV commercial detection has become an indispensable part of content-based video analysis technique due to the explosive growth in TV commercial volume. In this paper, a multi-modal (i.e. visual, audio and textual modalities) commercial digesting scheme is proposed to alleviate two challenges in commercial detection, which are the generation of mid-level semantic descriptor and the application...
Natural scene categorization (NSC) is an important and challenging task. Several state-of-the-art NSC systems use a codebook of visual terms to characterize images with the statistic of visual word counts. However, some kind of codebook generally tends to be more favorable for characterizing a special scene category, which takes either flat property or salient one. To obtain the good tradeoff performance...
Automatic image annotation (AIA) is an effective technology for improving the image retrieval. In this paper, a novel annotation scheme based on neural network (NN) is first proposed for characterizing the hidden association between two modalities, i.e. the visual and the textual modalities. Furthermore, latent semantic analysis (LSA) is employed to the NN based annotation scheme (noted as LSA-NN)...
Generally speaking, several aspects related to relevance feedback based CBIR include what means should be adopted for approximate semantic description of image content, what strategies be applied to sample labeling in feedback and what relevance model would be built for online discrimination. Using random sampling strategy, we construct a set of random subspaces for learning multiple intrinsic descriptions...
Automatic image annotation (AIA) is a promising way to improve the performance of image retrieval. In this paper, we propose a novel AIA scheme based on multiple-instance learning (MIL). By introducing the minimum reference set (MRS) into MIL (denoted by MRS-MIL), the positive instances (i.e. regions in images) embedded in the positive bags (i.e. images) can be picked out via reliable inferring for...
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