TY - GEN
T1 - From frequent itemsets to semantically meaningful visual patterns
AU - Yuan, Junsong
AU - Wu, Ying
AU - Yang, Ming
PY - 2007
Y1 - 2007
N2 - Data mining techniques that are successful in transaction and text data may not be simply applied to image data that contain high-dimensional features and have spatial structures. It is not a trivial task to discover meaningful visual patterns in image databases, because the content variations and spatial dependency in the visual data greatly challenge most existing methods. This paper presents a novel approach to coping with these difficulties for mining meaningful visual patterns. Specifically, the novelty of this work lies in the following new contributions: (1) a principled solution to the discovery of meaningful itemsets based on frequent itemset mining; (2) a self-supervised clustering scheme of the high-dimensional visual features by feeding back discovered patterns to tune the similarity measure through metric learning; and (3) a pattern summarization method that deals with the measurement noises brought by the image data. The experimental results in the real images show that our method can discover semantically meaningful patterns efficiently and effectively.
AB - Data mining techniques that are successful in transaction and text data may not be simply applied to image data that contain high-dimensional features and have spatial structures. It is not a trivial task to discover meaningful visual patterns in image databases, because the content variations and spatial dependency in the visual data greatly challenge most existing methods. This paper presents a novel approach to coping with these difficulties for mining meaningful visual patterns. Specifically, the novelty of this work lies in the following new contributions: (1) a principled solution to the discovery of meaningful itemsets based on frequent itemset mining; (2) a self-supervised clustering scheme of the high-dimensional visual features by feeding back discovered patterns to tune the similarity measure through metric learning; and (3) a pattern summarization method that deals with the measurement noises brought by the image data. The experimental results in the real images show that our method can discover semantically meaningful patterns efficiently and effectively.
KW - Image data mining
KW - Meaningful itemset mining
KW - Pattern summarization
KW - Self-supervised clustering
UR - http://www.scopus.com/inward/record.url?scp=36849090921&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=36849090921&partnerID=8YFLogxK
U2 - 10.1145/1281192.1281284
DO - 10.1145/1281192.1281284
M3 - Conference contribution
AN - SCOPUS:36849090921
SN - 1595936092
SN - 9781595936097
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 864
EP - 873
BT - KDD-2007
T2 - KDD-2007: 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Y2 - 12 August 2007 through 15 August 2007
ER -