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Wang W.,Shandong University | Sun J.,Shandong University | Sun J.,Hisense State Key Laboratory of Digital Media Technology | Liu J.,Shandong University
International Conference on Communication Technology Proceedings, ICCT | Year: 2015

In this paper, a memorability feature based-video hashing is proposed as an alternative to appearance feature and visual attention based-algorithms. Inspired by our previous study which shows spatial histograms based on visual attention have positive influence on predicting image memorability, we propose to define the memory feature (MF) and use it to characterize the video content. The saliency map (SM) is constructed by the visual saliency detection in video segments and three local visual attention regions are detected from SM. The spatial histograms feature of the visual attention regions are obtained, which are defined as memory feature (MF) and the supervised hashing with kernels (KSH) is adopted to map the MF to hash. Experiments on different kinds of videos demonstrate that the proposed MF-Hashing algorithm is promising in video hashing. © 2015 IEEE. Source


Sun J.,Peking University | Sun J.,Shandong University | Sun J.,Hisense State Key Laboratory of Digital Media Technology | Xie J.,Shandong University | And 2 more authors.
Communications in Computer and Information Science | Year: 2012

During 2D to 3D conversion, key-frame selection is a very important step as it can directly affect the visual quality of the 3D video. In this paper, a novel key-frame selection method for 2D-to-3D conversion is presented to get fewer errors and much better photorealistic perception. Firstly, the occlusion areas between two consecutive frames are detected and SURF-feature points of the frames are extracted. Secondly, the ratio of feature points to the correspondence is calculated, which is used to select the key-frame candidates. Finally, camera projection matrix in the projective space is computed for every key-frame candidate, and the key-frame candidate that has the least re-projection error is selected as the key-frame. Experimental results show that the propagated depth maps using the proposed method have fewer errors, which is beneficial to generate high quality stereoscopic video. © 2012 Springer-Verlag Berlin Heidelberg. Source


Nie X.-S.,Shandong University of Finance and Economics | Liu J.,Shandong University | Liu J.,Hisense State Key Laboratory of Digital Media Technology | Sun J.-D.,Shandong University | Sun J.-D.,Hisense State Key Laboratory of Digital Media Technology
Tien Tzu Hsueh Pao/Acta Electronica Sinica | Year: 2013

In order to identify video copies on the Internet, a video fingerprinting algorithm based on binary tree and stochastic neighbor embedding is proposed in this paper. In this scheme, representative frames are selected based on binary tree and normal cut, and the Discrete Cosine Transformation (DCT) coefficients of luminance of representative frames are taken as the high dimensional features of the video. The features are mapped into three-dimensional space using stochastic neighbor embedding. A matching key is generated based on the mean and variance of distance vector between adjacent points in this three-dimensional space, and the video fingerprint is generated by binarizing. Moreover, during video fingerprint matching, the matching key is used in the first-stage matching to reduce the search range, and then a further matching is carried out in the candidate fingerprint to identify video copies. Source


Sun J.,Peking University | Sun J.,Shandong University | Sun J.,Hisense State Key Laboratory of Digital Media Technology | Wang J.,Shandong University | And 4 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2013

In this paper, an unequally weighted video hashing algorithm is presented, in which visual saliency is used to generate the video hash and weight different hash bits. The proposed video hash is fused by two hashes, which are the spatio-temporal hash (ST-Hash) generated according to the spatio-temporal video information and the visual hash (V-Hash) generated according to the visual saliency distribution. In order to emphasize the contribution of visual salient regions to video content, Weighted Error Rate (WER) is defined as an unequally weighted hash matching method to take the place of BER. The WER, unlike BER, gives hash bits unequal weights according to their corresponding visual saliency in hash matching. Experiments verify the robustness and discrimination of the proposed video hashing algorithm and show that the WER-based hash matching is helpful to achieve better precision rate and recall rate. © Springer-Verlag 2013. Source


Liu X.,Shandong University | Sun J.,Shandong University | Sun J.,Hisense State Key Laboratory of Digital Media Technology | Liu J.,Shandong University | Liu J.,Hisense State Key Laboratory of Digital Media Technology
IEEE Signal Processing Letters | Year: 2013

The video hash derived from the temporally representative frame (TRF) has attracted increasing interests recently. A temporally visual weighting (TVW) method based on visual attention is proposed for the generation of TRF in this paper. In the proposed TVW method, the visual attention regions of each frame are obtained by combining the dynamic and static attention models. The temporal weight for each frame is defined as the strength of temporal variation of visual attention regions and the TRF of a video segment can be generated by accumulating the frames by the proposed TVW method. The advantage of the TVW method is proved by the comparison experiments. The video hashes used for comparison are derived from the TRFs, which are generated based on the proposed TVW method and other existing weighting methods respectively. The experimental results show that the TVW method is helpful to enhance the robustness and discrimination of video hash. © 1994-2012 IEEE. Source

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