Automatic image annotation retrieval system

Pei Cheng Cheng*, Been Chian Chien, Hao Ren Ke, Wei Pang Yang

*此作品的通信作者

研究成果: 雜誌貢獻期刊論文同行評審

摘要

Content-based image retrieval (CBIR) is a group of techniques that analyzes the visual features, such as color, shape, and texture, of an example image or image sub-region to find similar images in an image database. Query by image example or stretch sometime for users is inconvenient. In this paper, we proposed an automatic annotation image retrieval system allowing users query image by keyword. In our system, an image was segmented into regions, each of which corresponds to an object. The regions identified by region-based segmentation are more consistent with human cognition than those identified by block-based segmentation. According to the object's visual features (color and shape), new objects will be mapped to the similar clusters to obtain their associated semantic concept. The semantic concepts derived by the training images may not be the same as the real semantic concepts of the underlying images, because the former concepts depend on the low-level visual features. To ameliorate this problem, we also propose a relevance-feedback model to learn the interests of users. The experiments show that the proposed algorithm outperforms the traditional co-occurrence model about 14.48%; furthermore, after five times of relevance feedback, the mean average precision is improved from 42.7% to 62.7%.

原文英語
頁(從 - 到)984-991
頁數8
期刊WSEAS Transactions on Communications
5
發行號6
出版狀態已發佈 - 2006 六月
對外發佈

ASJC Scopus subject areas

  • 電腦科學應用
  • 電腦網路與通信
  • 電氣與電子工程

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