In this paper, we propose a fast coarse-to-fine video retrieval scheme using shot-level spatio-temporal statistics. The scheme consists of a two-step coarse search followed by a fine search. In the coarse search stage, the shot-level motion and color distribution is computed as spatio-temporal features for shot matching. The first-step coarse search uses the shot-level global statistics to reduce the size of the search space drastically. By adding an adjacent shot of the first query shot, the second-step coarse search introduces a "causality" relation between two consecutive shots to improve the search accuracy. Finally, the fine-search step refines the search result by using the local color features extracted from the key frames of the query shots. Our experimental results show that the proposed method achieves good retrieval performance with a much reduced complexity compared to single-pass methods.

Due to the popularity of the Internet and the powerful computing capability of computers, efficient processing/retrieval of multimedia data has become an important issue. In this paper, we propose a fast video retrieval algorithm that bases its search core on the statistics of object motion. The algorithm starts with extracting object motions from a shot and then transforms/quantizes them into the form of probability distributions. By choosing the shot that has the largest entropy value among the constituent shots of an unknown query video clip, we execute the first stage video search. By comparing two shots with different lengths, their corresponding motion probability distributions are compared by a discrete Bhattacharyya distance which is designed to measure the similarity between any two distribution functions. In the second stage, we add an adjacent shot (either preceding or subsequent) to perform a finer comparison. Experimental results demonstrate that our fast video retrieval algorithm is powerful in terms of accuracy and efficiency.

This paper presents a new video-coding technique based on displaced frame difference (DFD) prediction and multiresolution block matching for layered transmission. Starting from the base layer, the algorithm encodes one layer at a time until the design of the top layer is completed. For the encoding of each layer, the DFD prediction and multistage motion-vector quantization (MSMVQ) techniques are employed so that the results of DFD encoding and block matching at previous layers can be used to enhance the coding efficiency at that layer. Numerical results show that the algorithm is effective for the applications where high rate-distortion performance and low computational complexity for layered video transmission are required.