Paper Info
Title
Device parts retrieval from assembly drawings with SVM based active relevance feedback
Abstract
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.
Year
DOI
Venue
2007
10.1145/1282280.1282337
CIVR
Keywords
Field
DocType
device part,retrieval performance,feedback image,device parts retrieval,novel active selection criterion,later retrieval,specified device part,application area,relevance feedback,active relevance feedback,novel solution,assembly drawing,clustering
Computer vision,Object detection,Relevance feedback,Pattern recognition,Information retrieval,Computer science,Support vector machine,Selection criterion,Artificial intelligence,Cluster analysis,Machine learning,Sample size determination
Conference
Citations 
PageRank 
References 
1
0.36
14
Authors
5
Name
Order
Citations
PageRank
Rujie Liu114715.49
Takayuki Baba2778.19
Yusuke Uehara3628.15
Daiki Masumoto4766.33
Shigemi Nagata5747.16