A cost model for nearest neighbor search in high-dimensional data space
PODS '97 Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
ORF-NT: an object-based image retrieval framework using neighborhood trees
ISCIS'05 Proceedings of the 20th international conference on Computer and Information Sciences
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This study deals with designing a flexible feature space for Content Based Image retrieval Systems (CBIR). For this purpose, initially, a large variety of features are extracted from the regions of the pre-segmented images. Then, the feature set of each object class is learned using the Fuzzy Art Map Architecture, by identifying the weights of each feature for each object class. In the querying phase, trained set of feature weights are used to find the label of each object class. This task is achieved by combining the regions in the images and computing the maximum membership value for the compound regions, which correspond to a possible object class.