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Visual attention analysis provides an alternative methodology to semantic image understanding in many applications such as adaptive content delivery and region-based image retrieval. In this paper, we propose a feasible and fast approach to attention area detection in images based on contrast analysis. The main contributions are threefold: 1) a new saliency(More)
Author(s): Yu-Fei Ma (yfma@microsoft.com) Xian-Sheng Hua (xshua@microsoft.com) Lie Lu (llu@microsoft.com) Hong-Jiang Zhang (hjzhang@microsoft.com) Affiliation(s): Microsoft Research Asia, 5/F, Beijing Sigma Center, 49 Zhichun Road, Haidian District, Beijing (100080), P.R. China TEL: (8610) 62617711 FAX: (8610) 62555337 ABSTRACT Due to the information(More)
Automatic generation of video summarization is one of the key techniques in video management and browsing. In this paper, we present a generic framework of video summarization based on the modeling of viewer's attention. Without fully semantic understanding of video content, this framework takes advantage of understanding of video content, this framework(More)
In this paper, we propose a unified approach for video summarization based on the analysis of video structures and video highlights. Two major components in our approach are scene modeling and highlight detection. Scene modeling is achieved by normalized cut algorithm and temporal graph analysis, while highlight detection is accomplished by motion attention(More)
Moving object detection is an important task in video analysis. In this paper, we propose a new method for detecting moving objects based on spatio-temporal entropy. By measuring color variation in multiple successive frames, a spatio-temporal entropy image (STPI) is formed. Then the morphological methodology is employed to extract salient motions or moving(More)