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In this paper, an effective low bit-rate video coding scheme is developed to realize state-of-the-art video coding efficiency with lower encoder complexity, while supporting standard compliance and error resilience. Such an architecture is particularly attractive for application scenarios involving resource-deficient wireless video communications. At the encoder, in order to increase resilience to channel error, multiple descriptions of a video sequence are generated in the spatio-temporal domain by temporal multiplexing and spatial adaptive downsampling. The resulting side descriptions are interleaved with each other in temporal domain, while still with conventional square sample grids in spatial domain. As such, each side description can be compressed without any change to existing video coding standards. At the decoder, each side description is first decompressed, and then reconstructed to the original resolution with the help of the other side description. In this procedure, the decoder recovers the original video sequence in a constrained least squares regression process, in which 2D or 3D piecewise autoregressive model is adaptively chosen according to different predictive modes. In this way, the spatial and temporal correlation is sufficiently explored to achieve superior quality. Experimental results demonstrate that the proposed video coding scheme outperforms H.264/AVC and other state-of-the-art methods in rate–distortion performance at low bit-rates and achieves superior visual quality at medium bit rates as well, while with lower encoding computational complexity. [ABSTRACT FROM AUTHOR]

In sign language recognition (SLR), the major challenges now are developing methods that solve signer-independent continuous sign problems. In this paper, SOFM/HMM is first presented for modeling signer-independent isolated signs. The proposed method uses the self-organizing feature maps (SOFM) as different signers'' feature extractor for continuous hidden Markov models (HMM) so as to transform input signs into significant and low-dimensional representations that can be well modeled by the emission probabilities of HMM. Based on these isolated sign models, a SOFM/SRN/HMM model is then proposed for signer-independent continuous SLR. This model applies the improved simple recurrent network (SRN) to segment continuous sign language in terms of transformed SOFM representations, and the outputs of SRN are taken as the HMM states in which the lattice Viterbi algorithm is employed to search the best matched word sequence. Experimental results demonstrate that the proposed system has better performance compared with conventional HMM system and obtains a word recognition rate of 82.9% over a 5113-sign vocabulary and an accuracy of 86.3% for signer-independent continuous SLR. [Copyright &y& Elsevier]

In this paper, we propose a novel 3D object retrieval with features collaboration and bipartite graph matching strategies. We explored the essential characters of 3D object in a view-based retrieval framework, which extracts complement descriptors from both the contour and the interior region of 3D object effectively. Specifically, a greedy bipartite graph matching algorithm is employed. With the bipartite graph matching and feature concatenation, significant performance improvement is achieved in the 3D object retrieval task. The proposed method is evaluated by the third party on the data set comprising more than 500 3D objects and achieves the best performance for SHREC’15 challenge. [ABSTRACT FROM AUTHOR]

In this paper, the divide-and-conquer based hierarchical video compressive sensing (CS) coding framework is proposed, in which the whole video is independently divided into non-overlapped blocks of the hierarchical frames. The proposed framework outperforms the traditional framework through the better exploitation of frames correlation with reference frames, the unequal sample subrates setting among frames in different layers and the reduction of the error propagation. At the encoder, compared with the video/frame based CS, the proposed hierarchical block based CS matrix can be easily implemented and stored in hardware. Each measurement of the block in a different hierarchical frame is obtained with the different sample subrate. At the decoder, by considering the spatial and temporal correlations of the video sequence, a spatial–temporal sparse representation based recovery is proposed, in which the similar blocks in the current frame and these recovered reference frames are organized as a spatial–temporal group unit to be represented sparsely. Finally, the recovery problem of video compressive sensing coding can be solved by adopting the split Bregman iteration. Experimental results show that the proposed method achieves better performance against many state-of-the-art still-image CS and video CS recovery algorithms. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we present an instant action recognition method, which is able to recognize an action in real-time from only two continuous video frames. For the sake of instantaneity, we employ two types of computationally efficient but perceptually important features – optical flow and edges – to capture motion and shape characteristics of actions. It is known that the two types of features can be unreliable or ambiguous due to noise and degradation of video quality. In order to endow them with strong discriminative power, we pursue combined features, of which the joint distributions are different in-between action classes. As the low-level visual features are usually densely distributed in video frames, to reduce computational expense and induce a compact structural representation, we propose to first group the learned discriminative joint features into feature groups according to their correlation, then adapt the efficient boosting method as the action recognition engine which take the grouped features as input. Experimental results show that the combination of the two types of features achieves superior performance in differentiating actions than that of using each single type of features alone. The whole model is computationally efficient, and the action recognition accuracy is comparable to the state-of-the-art approaches. [Copyright &y& Elsevier]

Abstract: In this paper, we propose an object detection approach using spatial histogram features. As spatial histograms consist of marginal distributions of an image over local patches, they can preserve texture and shape information of an object simultaneously. We employ Fisher criterion and mutual information to measure discriminability and features correlation of spatial histogram features. We further train a hierarchical classifier by combining cascade histogram matching and support vector machine. The cascade histogram matching is trained via automatically selected discriminative features. A forward sequential selection method is presented to construct uncorrelated and discriminative feature sets for support vector machine classification. We evaluate the proposed approach on two different kinds of objects: car and video text. Experimental results show that the proposed approach is efficient and robust in object detection. [Copyright &y& Elsevier]

Abstract: Text in images and video frames carries important information for visual content understanding and retrieval. In this paper, by using multiscale wavelet features, we propose a novel coarse-to-fine algorithm that is able to locate text lines even under complex background. First, in the coarse detection, after the wavelet energy feature is calculated to locate all possible text pixels, a density-based region growing method is developed to connect these pixels into regions which are further separated into candidate text lines by structural information. Secondly, in the fine detection, with four kinds of texture features extracted to represent the texture pattern of a text line, a forward search algorithm is applied to select the most effective features. Finally, an SVM classifier is used to identify true text from the candidates based on the selected features. Experimental results show that this approach can fast and robustly detect text lines under various conditions. [Copyright &y& Elsevier]