Semantic clustering and querying on heterogeneous features for visual data

Gholamhosein Sheikholeslami, Wendy Chang, Aidong Zhang
1998 Proceedings of the sixth ACM international conference on Multimedia - MULTIMEDIA '98  
The e ectiveness of the content-based image retrieval can be enhanced using the heterogeneous features embedded in the images. However, since the features in texture, color, and shape are generated using di erent computation methods and thus may require di erent similarity measurements, the integration of the retrieval on heterogeneous features is a non-trivial task. In this paper, we present a semanticsbased clustering approach, termed SemQuery, to support visual queries on heterogeneous
more » ... es of images. Using this approach, the database images are classi ed based on their heterogeneous features. Each semantic image cluster contains a set of subclusters that are represented by the heterogeneous features that the images contain. A database image is included into a feature subcluster only if the image contains all the features under the same cluster. We also designed a multi-layer model to merge the results of basic queries on individual features. A visual query processing strategy is then presented to support visual queries on heterogeneous features. Experimental analysis is conducted and presented to demonstrate the e ectiveness and e ciency of the proposed approach.
doi:10.1145/290747.290749 dblp:conf/mm/SheikholeslamiCZ98 fatcat:5a3a3uw7ovgsbl2bnt5s6yge2m