Interactive image retrieval using constraints

被引:10
|
作者
Jian, Meng [1 ]
Jung, Cheolkon [1 ]
Shen, Yanbo [1 ]
Liu, Juan [1 ]
机构
[1] Xidian Univ, Minist Educ, Key Lab Intelligent Percept & Image Understanding, Xian 710071, Peoples R China
基金
中国国家自然科学基金;
关键词
Active learning; Adaptive constraint propagation; Interactive image retrieval; Pairwise constraints; Relevance feedback; Seed propagation; MEAN SHIFT;
D O I
10.1016/j.neucom.2015.02.040
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The proper use of constraints improves the data clustering performance. In this paper, we propose a novel interactive image retrieval framework using constraints. First, we extract the user's region of interest (ROI) from queries by simple user interaction using adaptive constraints-based seed propagation (ACSP), and obtain initial retrieval results based on the ROI. Then, we improve the retrieval results by active learning from the user's relevance feedback using ACSP. Since ACSP is very effective in propagating the user's interactive information of constraints by employing a kernel learning strategy, it successfully learns the correlation between low-level image features and high-level semantics from the ROI and relevance feedbacks. Experimental results demonstrate that the proposed framework remarkably improves the image retrieval performance by ACSP-based constraint propagation in terms of both effectiveness and efficiency. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码:210 / 219
页数:10
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