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Recently, considerable efforts have been made in visual search reranking towards refining initial text-based image search results. In this paper, we propose a hypergraph based semi-supervised ranking method called HyperSSR for image search reranking. According to the basic visual consistency principle that visually similar images should have similar ranking scores, we introduce the hypergraph to capture the intrinsic geometrical structure of the data distribution. To build a robust hypergraph, a novel hypergraph construction approach is developed to incorporate relevance and pseudo relevance degree information from labeled and unlabeled samples, respectively. Based on the premise that a ranking model should work better with the prior pairwise preferences, we jointly incorporate the hypergraph regularizer and the prior pairwise preferences information into a unified ranking learning framework. Experimental results on MSRA-MM 1.0 dataset suggest our proposed approach produces superior performances compared with several state-of-the-art methods.

The task of video sequence classification plays a critical role in the development of computer vision. Considering this fact, this letter proposes a novel tensor decomposition method called tensor-driven temporal correlation in which general tensors are used as input for video sequence classification. Because distortion and redundancy may exist in the tensor representations of video sequences, we project the original tensor into subspaces spanned by spatial basis matrices in the proposed formulation. Moreover, to better preserve the temporal smoothness between consecutive slices of the tensor, the basis matrices are jointly learned by introducing an autoregressive model. An experiment on the commonly used Cambridge hand-gesture database demonstrates that our proposed method reaches convergence within a small number of iterations during the training stage and achieves promising results compared with state-of-the-art methods.

Classification of video sequences is an important task with many applications in video search and action recognition. As opposed to some traditional approaches that transform original video sequences into forms of visual feature vectors, tensor-based methods have been proposed for classifying video sequences with natural representation of original data. However, one obvious limitation of tensor-based methods is that the input video sequences are often required to be preprocessed with a unified length of time. In this paper, we propose a technique for handling classification of video sequences in unequal length of time, namely Spatial-Temporal Iterative Tensor Decomposition (S-TITD) for uniform length. The proposed framework contains two primary steps. We first represent original video sequences as a third-order tensor and perform Tucker-2 decomposition to obtain the reduced-dimension core tensor. Then we encode the third order of core tensor to a uniform length by adaptively selecting the most informative slices. Notably, the above two steps are embedded into a dynamic learning framework to guarantee the proposed method has the ability of updating results over time. We conduct a series of experiments on three public datasets in gesture and action recognition, and the experimental results show that the proposed S-TITD approach achieves better performances than the state-of-the-art algorithms.