In this paper, a music segmentation framework is proposed to segment music streams based on human perception. In the proposed framework, three perceptual features corresponding to four perceptual properties are extracted. By analyzing the trajectory of feature values, the cutting points of a music stream can be identified. According to the complementary characteristics of the three features, a ranking algorithm is designed to achieve a better accuracy. We perform a series of experiments to evaluate the Complementary Characteristics and the effectiveness of the proposed framework.
Motion track is an important feature to show the spatio-temporal relationship of a video object in a video. In this paper, we propose a novel motion track representation based on MPEG-7 motion descriptor. A new descriptor is proposed to represent the motion track in the X-Y plane and the trend of velocity changes. Moreover, a new similarity measure for comparing two motion tracks based on the motion trajectory and velocity differences is proposed. The trajectory is compared by the properties of the polynomials, and the velocity is compared by the different trends. Furthermore, the motion track segmentation method is proposed to handle a complicated motion behavior and the relevance feedback is used to improve the query results. Experiment results show that this approach has a higher precision than existing approaches.
In this paper, a video data model is proposed to represent the content of video data. In the proposed model, the trajectory and other properties of objects are recorded. From the trajectory, the motion event such as 'high speed' of an object and 'increasing distance' between objects can be automatically derived. A query language named V-SQL based on the video data model is also proposed for the users to describe the content of the desired video clips. A graphical user interface is implemented for an easier query specification.
With the advance of multimedia technologies and the explosive expansion of the World Wide Web, the volume of image and video data increases rapidly. An efficient and effective multimedia data retrieval technique is needed. In this paper, we propose an approach based on feature points for the content-based image retrieval. The feature points extracted from the multiresolution representation of the query image and database image are first matched to determine the matching pairs. Then, the marching pairs are classified into groups. Finally, two similarity measurements based on different similarity requirements are proposed to compute the similarity degree. We perform a series of experiments to study the characteristics of this approach, and compare with the region-based approach on similar-shot sequence retrieval. The comparison shows the superiority of this approach.
KEYWORDS: RGB color model, Image retrieval, Databases, Quantization, Visualization, Feature extraction, Information visualization, Visual process modeling, Adaptive optics, Internet
The amount of pictorial data grows enormously with the expansion of the WWW. From the large number of images, it is very important for users to retrieve desired images via an efficient and effective mechanism. In this paper we prose two efficient approaches to facilitate image retrieval by using a simple method to represent the image content. Each image is partitioned into m X n equal-sized sub-images. A color that has enough number of pixels in a block is extracted to represent its content. In the first approach, the image content is represented by the extracted colors of the blocks. The spatial information of images is considered in image retrieval. In the second approach, the colors of the blocks in an image are used to extract objects. A block- level process is process is proposed to perform the region extraction. The spatial information of regions is considered unimportant in image retrieval. Our experiments show that these two block-based approaches can speed up the image retrieval. Moreover, the two approaches are effective for different requirements of image similarity. Users can choose a proper approach to process their queries based on their similarity requirements.
In this paper, we propose four index structures for music data retrieval. Based on suffix trees, we develop two index structures called combined suffix tree and independent suffix trees. These methods still show shortcomings for some search functions. Hence we develop another index, called Twin Suffix Trees, to overcome these problems. However, the Twin Suffix Trees lack of scalability when the amount of music data becomes large. Therefore we propose the fourth index, called Grid-Twin Suffix Trees, to provide scalability and flexibility for a large amount of music data. For each index, we can use different search functions, like exact search and approximate search, on different music features, like melody, rhythm or both. We compare the performance of the different search functions applied on each index structure by a series of experiments.
Building a system for the users on the Internet to search interested resources or to provide guided browsing services has formed an important trend as a result of the rapid accumulation of information, the booming development of information system and the increasing requirement of resource sharing. In this paper, we present the Integrated resource Query and guidedBrowsing System (IQBS), which has been implemented at our laboratory, to achieve this goal. The scheme we proposed takes advantage of the strong power of the database system to structure and query data, and greatly promote the qua!ity and performance of searching resources in Internet. In addition, mechanisms are designed to guide the users on the Internet to conveniently browse resources with a reduced traffic cost. Keywords: Internet resource discovery, World wide web, Query processing, Rough set, Guided browsing, Supervised learning, Hypertext management.
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