Mr. Ye Shen Profile

Ye Shen

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Proceedings Article | 10 December 2002 Paper

Matching pursuit decomposition of speech signals for compact representation

Ye Shen, Hongmei Ai, C.-C. Jay Kuo

Proceedings Volume 4861, (2002) https://doi.org/10.1117/12.456500

KEYWORDS: Associative arrays, Chemical species, Signal to noise ratio, Quantization, Signal processing, Time-frequency analysis, Frequency modulation, Video coding, Evolutionary algorithms, Image processing

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Matching Pursuit (MP) expands a signal over an overcomplete dictionary of normalized atoms in an iterative fashion. A careful selection of dictionary components is critical in the design of the MP algorithm for compact signal representation and manipulation. In this research, the use of MP as an alternative waveform-coding scheme for speech signals is investigated. The improvement of MP over conventional transform coding schemes is due to the use of overcomplete basis functions. Furthermore, the performance of MP representation can be enhanced via a compact MP dictionary obtained from training. Inspired by the popular Vector Quantization (VQ) algorithm, a dictionary-training algorithm is proposed in this paper to find the optimal dictionary for MP in speech coding. The MP decomposition with a trained dictionary is shown to improve the compactness of speech representation over the traditional MP decomposition with a generic Gabor dictionary. A better SNR performance is achieved with a dictionary of a limited size, which has a good potential for future appliations.

Proceedings Article | 5 October 1998 Paper

Online scene change detection of multicast (MBone) video

Wensheng Zhou, Ye Shen, Asha Vellaikal, C.-C. Jay Kuo

Proceedings Volume 3527, (1998) https://doi.org/10.1117/12.325821

KEYWORDS: Video, Video processing, Detection and tracking algorithms, Internet, Multimedia, Video compression, Telecommunications, Cameras, Semantic video, Feature extraction

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Many multimedia applications, such as multimedia data management systems and communication systems, require efficient representation of multimedia content. Thus semantic interpretation of video content has been a popular research area. Currently, most content-based video representation involves the segmentation of video based on key frames which are generated using scene change detection techniques as well as camera/object motion. Then, video features can be extracted from key frames. However most of such research performs off-line video processing in which the whole video scope is known as a priori which allows multiple scans of the stored video files during video processing. In comparison, relatively not much research has been done in the area of on-line video processing, which is crucial in video communication applications such as on-line collaboration, news broadcasts and so on. Our research investigates on-line real-time scene change detection of multicast video over the Internet. Our on-line processing system are designed to meet the requirements of real-time video multicasting over the Internet and to utilize the successful video parsing techniques available today. The proposed algorithms extract key frames from video bitstreams sent through the MBone network, and the extracted key frames are multicasted as annotations or metadata over a separate channel to assist in content filtering such as those anticipated to be in use by on-line filtering proxies in the Internet. The performance of the proposed algorithms are demonstrated and discussed in this paper.

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