Discovering spatial associations in images

In this paper, our focus in data mining is concerned with the discovery of spatial associations within images. Our work concentrates on the problem of finding associations between visual content in large image databases. Discovering association rules has been the focus of many studies in the last few years. However, for multimedia data such as images or video frames, the algorithms proposed in the literature are not sufficient since they miss relevant frequent item-sets due to the peculiarity of visual data, like repetition of features, resolution levels, etc. We present in this paper an approach for mining spatial relationships from large visual data repositories. The approach proceeds in three steps: feature localization, spatial relationship abstraction, and spatial association discovery. The mining process considers the issue of scalability and contemplates various feature localization abstractions at different resolution levels.

[1]  Jiawei Han,et al.  Discovery of Multiple-Level Association Rules from Large Databases , 1995, VLDB.

[2]  Sridhar Ramaswamy,et al.  On the Discovery of Interesting Patterns in Association Rules , 1998, VLDB.

[3]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[4]  William Frawley,et al.  Knowledge Discovery in Databases , 1991 .

[5]  Usama M. Fayyad,et al.  Automating the Analysis and Cataloging of Sky Surveys , 1996, Advances in Knowledge Discovery and Data Mining.

[6]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.

[7]  Jiawei Han,et al.  Meta-Rule-Guided Mining of Association Rules in Relational Databases , 1995, KDOOD/TDOOD.

[8]  Hisashi Nakamura,et al.  Fast Spatio-Temporal Data Mining of Large Geophysical Datasets , 1995, KDD.

[9]  Laks V. S. Lakshmanan,et al.  Exploratory mining and pruning optimizations of constrained associations rules , 1998, SIGMOD '98.

[10]  A. Voisard.  Spatial Query Languages , 2002 .

[11]  Rajeev Motwani,et al.  Beyond market baskets: generalizing association rules to correlations , 1997, SIGMOD '97.

[12]  Andrzej Czyzewski,et al.  Mining Knowledge in Noisy Audio Data , 1996, KDD.

[13]  Pietro Perona,et al.  Modeling Subjective Uncertainty in Image Annotation , 1996, Advances in Knowledge Discovery and Data Mining.

[14]  Jiawei Han,et al.  Resource and knowledge discovery from the internet and multimedia repositories , 1999 .

[15]  Jiawei Han,et al.  Mining recurrent items in multimedia with progressive resolution refinement , 2000, Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073).

[16]  William I. Grosky,et al.  Segmentation and representation of lesions in MRI brain images , 1999, Medical Imaging.

[17]  Heikki Mannila,et al.  Efficient Algorithms for Discovering Association Rules , 1994, KDD Workshop.

[18]  Ze-Nian Li,et al.  Illumination Invariance and Object Model in Content-Based Image and Video Retrieval , 1999, J. Vis. Commun. Image Represent..

[19]  Jiawei Han,et al.  Mining MultiMedia Data , 1999 .

[20]  M. Egenhofer,et al.  Topological Relations Between Regions in IR 2 and ZZ 2 * , 1993 .

[21]  Christos Davatzikos,et al.  Mining lesion-deficit associations in a brain image database , 1999, KDD '99.