Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Parameter-Free Geometric Document Layout Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Analysis of Engineering Drawings: State of the Art and Challenges
GREC '97 Selected Papers from the Second International Workshop on Graphics Recognition, Algorithms and Systems
Enhancing relevance feedback in image retrieval using unlabeled data
ACM Transactions on Information Systems (TOIS)
Random sampling based SVM for relevance feedback image retrieval
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Relaxed lightweight assembly retrieval using vector space model
Computer-Aided Design
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Content based assembly drawing retrieval is valued highly in many application areas, and it is a common thing to seek assembly drawings from a large collection where a specified device part is contained. Different from object detection techniques, a novel solution is presented in this paper to find the occurrences of target objects. Firstly, all device parts are extracted from assembly drawing images according to their specific characteristics. In later retrieval, these parts are compared with the query image to realize the search task. Furthermore, SVM based relevance feedback is adopted to incrementally improve the retrieval performance, and two strategies are proposed: (1) a novel active selection criterion, which takes into consideration both the informative and the representative measures to obtain more information from the feedback images; (2) incorporation of unlabeled images to alleviate the small sample size problem. The performance of this method is verified by extensive experiments.