Digital image processing
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual information retrieval from large distributed online repositories
Communications of the ACM
An automatic hierarchical image classification scheme
MULTIMEDIA '98 Proceedings of the sixth ACM international conference on Multimedia
Data Mining: Introductory and Advanced Topics
Data Mining: Introductory and Advanced Topics
Data Resource Selection in Distributed Visual Information Systems
IEEE Transactions on Knowledge and Data Engineering
Configuration based scene classification and image indexing
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A comparison of wavelet transform features for texture image annotation
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2 - Volume 2
Hidden semantic concept discovery in region based image retrieval
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Self organization of a massive document collection
IEEE Transactions on Neural Networks
Mining images on semantics via statistical learning
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Minimal document set retrieval
Proceedings of the 14th ACM international conference on Information and knowledge management
Classify By Representative Or Associations (CBROA): a hybrid approach for image classification
MDM '05 Proceedings of the 6th international workshop on Multimedia data mining: mining integrated media and complex data
Knowledge discovery in multimedia repositories: the role of metadata
MMACTE'05 Proceedings of the 7th WSEAS International Conference on Mathematical Methods and Computational Techniques In Electrical Engineering
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This paper proposes a data mining approach to modeling relationships among categories in image collection. In our approach, with image feature grouping, a visual dictionary is created for color, texture, and shape feature attributes respectively. Labeling each training image with the keywords in the visual dictionary, a classification tree is built. Based on the statistical properties of the feature space we define a structure, called α-Semantics Graph, to discover the hidden semantic relationships among the semantic categories embodied in the image collection. With the α-Semantics Graph, each semantic category is modeled as a unique fuzzy set to explicitly address the semantic uncertainty and semantic overlap among the categories in the feature space. The model is utilized in the semantics-intensive image retrieval application. An algorithm using the classification accuracy measures is developed to combine the built classification tree with the fuzzy set modeling method to deliver semantically relevant image retrieval for a given query image. The experimental evaluations have demonstrated that the proposed approach models the semantic relationships effectively and the image retrieval prototype system utilizing the derived model is promising both in effectiveness and efficiency.