| Video on demand (VOD) is an important multimedia application based on streaming media technique. It attracts wide attention from the scholars in the field of video research. Users in video system want to consume video media in a self-adaptive way, and consume it anytime, anywhere and with any format. Content based video retrieval (CBVR) exerts outstanding effects in satisfying consumers' demands in VOD system. Unfortunately, traditional CBVR methods have some shortcomings. The main shortcoming is that the query form of video segment can not satisfy the universal and individual demands of users, and the video character extracted from the retrieval system can not represent the semantic information of users because of its lack of relativity and universality. This problem cumbers CBVR to achieve ideal performance. In this dissertation, all proposed methods are to solve this problem.Image segmentation is a key technique of the extraction of video semantics. In chapter 3, a novel image segmentation method for video retrieval is proposed. In this method, we first use the color information of the interesting region in example image to estimate the color distribution model of the to-be-segmented image's foreground and background. Then for each pixel, we estimate its similarity to the foreground and background. By integrating the pixel estimation with the histogram matching and contrast description of the objective foreground, we construct a novel graph-cut optimization framework. Compared with other algorithms, due to the depiction of each pixel's color likelihood, our method is more robust for the variety of illumination and the alteration of scale or color proportion of foreground. Experiments show that our method is more effective than the traditional histogram matching algorithm, and more competent for video retrieval.In chapter 4, we first point out the shortcomings of the traditional video retrieval format of video segment. Then, based on the algorithm to segment key frame proposed in chapter 4, we propose a novel type for video-on-demand (VOD). The user uses the interested part of single image as the query. The system server, which stores the video summary in the whole system, segments the frames in video summaries according to the query, and computes the distance between the image and the query. Furthermore, the server can localize and play the video that the user requested. The query can come from the poster or from the frame itself, since it only handles the region of interest (ROI), no constraints are required for the color of the background (out of ROI). Experiments show that it is a novel and effective style for video retrieval which can localize the source video and the congeneric video of the query accurately. This method can be applied in the VOD system.Existing video retrieval system always extract low level character (such as color, texture etc.) of video system. It results in incompetent effect of retrieval system. In chapter 5, we proposed a video retrieval method using scale-invariant feature transform (SIFT) based on the query format of interested part of single image that proposed in chapter 4. The user uses the interested part of a single image as the query. The system server, which stores all the video summaries, uses the scale-invariant feature transform (SIFT) to match the input query with the ones in the frames of video summaries. Then, the server can localize and retrieve the video that the user requested. Experiments show that this method can localize the source video and the congeneric video of the query accurately which is invariant to foreground scaling and rotation, and partially illumination invariant. This method can be applied in VOD system effectively.
|
PDF Full Text Request