From frequent itemsets to semantically meaningful visual patterns

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 ...

• Type: Conference
• Year: 2007
• Where: KDD
• Authors: Junsong Yuan, Ying Wu, Ming Yang
• Keywords: Data Mining, Image Data Mining, KDD 2007, Meaningful Itemset Mining, Meaningful Visual Patterns