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For the problems of low accuracy and high complexity in detection of gradual shot boundary and long shot, a new video shot boundary detection algorithm based on feature fusion and clustering technique (FFCT) is proposed. In the algorithm, the interval frames of video sequence are selected, converted to gray images and scaled by sampling. With the frames, the speed-up robust features (SURF) and fingerprint features are extracted from non-compressed domain and compressed domain, and then the extracted features are fused. Next, K-means method is used to cluster the fused features, and linear discriminant analysis (LDA) is introduced to map the clusters to realize cohesion within classes and looseness among classes. Finally, the correlation of the feature classes between frames is calculated, and the features in each class are selected through density calculation and matched to realize the coarse detection and fine detection of video shot boundary. In the experiment, compared with the latest representative algorithms, it has the highest accuracy for the proposed algorithm. In particular, the detection of gradual shot boundary and long shot are also more accurate. Meanwhile, the average time consumption is also reduced. The experimental results show that the proposed algorithm has high accuracy and time efficiency, especially for gradual shot boundary and long shot detection. INDEX TERMS Shot boundary detection, feature fusion, clustering, mapping.

In order to achieve content-based binocular stereoscopic image or video retrieval efficiently, a feature indexing algorithm based on hybrid grid multiple suffix tree and hierarchical clustering is proposed. With the RGB-D image model, the shape features of depth map obtained from the matching of binocular stereoscopic left image and right image and the color features of left image are extracted respectively. The features are quantified and hashed, and the optimized underlying features are sorted as leaf nodes of hierarchical indexing. Then the shape and color feature values of leaf nodes are mapped to the two-dimensional coordinates, and the 2D feature points are put into different grid hash areas respectively by clustering and labeled with multiple suffix tree. Furthermore, to construct the global index, a pointer to an array of clustering grid center point is defined according to the computation of grid area feature values. The experimental results show that compared to the double grid suffix tree and typical stereoscopic image feature indexing structure, the proposed algorithm can effectively reduce the indexing construction complexity While maintaining high recall, it can also greatly improve the query efficiency, which can better realize the feature indexing of binocular stereoscopic images or videos. INDEX TERMS Feature indexing, hierarchical clustering, hybrid grid multiple suffix tree, stereoscopic image.

Video Shot segmentation is the key technology in content-based video retrieval and browsing, and which will directly affect the results of video retrieval. In view of the problems that the traditional shot segmentation algorithm is complex, the feature of video frame is not ideal, and the segmentation accuracy is low, this paper proposes a shot segmentation algorithm based on SURF (Speeded up Robust Features). This algorithm obtains the boundary of shots by computing the SURF features of video frames and calculating the feature matching rate. The experimental results show that: this algorithm can greatly reduce the amount of computation and data, improve the efficiency of the algorithm. And this algorithm has a great improvement in the accuracy of shot segmentation compared with shot segmentation algorithm based on color histogram.

In the paper, an approach is proposed for the problem of consistency in depth maps estimation from binocular stereo video sequence. The consistent method includes temporal consistency and spatial consistency to eliminate the flickering artifacts and smooth inaccuracy in depth recovery. So the improved global stereo matching based on graph cut and energy optimization is implemented. In temporal domain, the penalty function with coherence factor is introduced for temporal consistency, and the factor is determined by Lucas-Kanade optical flow weighted histogram similarity constraint (LKWHSC). In spatial domain, the joint bilateral truncated absolute difference (JBTAD) is proposed for segmentation smoothing. The method can smooth naturally and uniformly in low-gradient region and avoid over-smoothing as well as keep edge sharpness in high-gradient discontinuities to realize spatial consistency. The experimental results show that the algorithm can obtain better spatial and temporal consistent depth maps compared with the existing algorithms.