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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/44232

Title: Using Relevance Feedback to Learn Visual Concepts from Image Instances
Authors: Jun-Wei Hsieh
Cheng-Chin Chiang
Yea-Shuan Huang
Contributors: 國立臺灣海洋大學:資訊工程學系
NTOU:Department of Computer Science and Engineering
Keywords: Feedback
Image retrieval
Read only memory
Content based retrieval
Iterative algorithms
Communication industry
Computer industry
Optimization methods
Date: 1999-09
Issue Date: 2017-11-16T02:56:54Z
Publisher: 10th International Conference on Image Analysis and Processing
Abstract: Abstract:This paper presents a novel method to retrieve images by learning the embedded visual concept from a set of given examples. Through a user's relevance feedback, the visual concept can be effectively learned to classify images which contain common visual entities. The learning process is started by providing a set of either positive or negative training examples and is then interactively adjusted according to the user's relevance feedback. In contrast to traditional methods, the proposed method utilizes a novel way to overcome the under-training problem which is frequently suffered in the learning process. Since no time-consuming optimization process is involved, the proposed method learns the visual concepts extremely fast. Therefore, the target concept can be learned on-line and is user-adaptable for effective retrieval of image contents. Experimental results are provided to prove the superiority of the proposed method.
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/44232
Appears in Collections:[資訊工程學系] 演講及研討會

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