Browsing by Author "Xu, Y. Y."

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An EM based multiple instance learning method for image classification
(2008-10-01)
Pao, H. T.
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Chuang, S. C.
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Xu, Y. Y.
;
Fu, Hsin-Chia
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資訊工程學系
In this paper, we propose an EM based learning algorithm to provide a comprehensive procedure for maximizing the measurement of diverse density on given multiple Instances. Furthermore, the new EM based learning framework converts an MI problem into a single-instance treatment by using EM to maximize the instance responsibility for the corresponding label of each bag. To learn a desired image class, a user may select a set of exemplar images and label them to be conceptual related (positive) or conceptual unrelated (negative) images. A positive image consists of at least one object that the user may be interested, and a negative image should not contain any object that the user may be interested. By using the proposed EM based learning algorithm, an image retrieval prototype system is implemented. Experimental results show that for only a few times of relearning cycles, the prototype system can retrieve user's favor images from WWW over Internet. (C) 2007 Published by Elsevier Ltd.
Constructing and application of multimedia TV news archives
(2007)
Pao, H. T.
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Xu, Y. Y.
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Chung, S. C.
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Fu, H. C.
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管理科學系
This paper addresses an integrated information mining techniques for broadcasting TV-news. The utilizes technique from the fields of acoustic, image, and video analysis, for information on news title, reporters and news background. The goal is to construct a compact yet meaningful abstraction of broadcast TV news, allowing users to browse through large amounts of data in a non-linear fashion with flexibility and efficiency. By using acoustic analysis, a news program can be partitioned into news and commercial clips, with 90% accuracy on a data set of 400 hours TV-news recorded off the air from July 2005 to August of 2006. By applying additional speaker identification and/or image detection techniques, each news stories can be segmented with a better accuracy of 95.92%. On screen captions and screen characters are recognized by video OCR techniques to produce the title of each news stories. Then keywords can be extracted from title to link related news contents on the WWW. In cooperation with facial and scene analysis and recognition techniques, OCR results can provide users with multimodal query on specific news stories.
Constructing and application of multimedia TV-news archives
(2008-10-01)
Pao, H. T.
;
Chen, Y. H.
;
Lai, P. S.
;
Xu, Y. Y.
;
Fu, Hsin-Chia
This paper addresses an integrated information mining techniques for broadcasting TV-news. This utilizes technique from the fields of acoustic, image, and video analysis, for information on news story title, newsman and scene identification. The goal is to construct a compact yet meaningful abstraction of broadcast TV-news, allowing users to browse through large amounts of data in a non-linear fashion with flexibility and efficiency. By adding acoustic analysis, a news program can be partitioned into news and commercial clips, with 90% accuracy on a data set of 400 It TV-news recorded off the air from July 2005 to August 2006. By applying speaker identification and/ or image detection techniques, each news stories can be segmented with a better accuracy of 95.92%. On-screen captions or subtitles are recognized by OCR techniques to produce the text title of each news stories. The extracted title words can be used to link or to navigate more related news contents on the WWW. In cooperation with facial and scene analysis and recognition techniques, OCR results can provide users with multimodal query on specific news stories. Some experimental results are presented and discussed for the system reliability, performance evaluation and comparison. (C) 2007 Published by Elsevier Ltd.
Image classification and indexing by EM based multiple-instance learning
(2007)
Pao, H. T.
;
Xu, Y. Y.
;
Chuang, S. C.
;
Fu, H. C.
;
管理科學系
In this paper, we propose an EM based Multiple-Instance learning algorithm for the image classification and indexing. To learn a desired image class, a set of exemplar images are selected by a user. Each example is labeled as conceptual related (positive) or conceptual unrelated (negative) image. A positive image consists of at least one user interested object, and a negative example should not contain any user interested object. By using the proposed learning algorithm, an image classification system can learn the user's preferred image class from the positive and negative examples. We have built a prototype system to retrieve user desired images. The experimental results show that for only a few times of relearning, a user can use the prototype system to retrieve favor images from the WWW over Internet.