Image retrieval using flexible image subblocks
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 2
Bridging the semanitic gap in image retrieval
Distributed multimedia databases
Graph indexing and retrieval based on median graphs
MCPR'10 Proceedings of the 2nd Mexican conference on Pattern recognition: Advances in pattern recognition
Image retrieval by content using segmentation approach
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
A geometric data structure applicable to image mining and retrieval
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part I
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This paper examines the use of a computational geometry-based spatial color indexing methodology for efficient and effective image retrieval. In this scheme, an image is evenly divided into a number of M*N non-overlapping blocks, and each individual block is abstracted as a unique feature point labeled with its spatial location, dominant hue, and dominant saturation. For each set of feature points labeled with the same hue or saturation, we construct a Delaunay triangulation and then compute the feature point histogram by discretizing and counting the angles produced by this triangulation. The concatenation of all these feature point histograms serves as the image index. An important contribution of this work is to encode the spatial color information using geometric triangulation, which is translation, rotation, and scale independent. We have implemented the proposed approach and have tested it over two image collections of 2000 JPEG images and 1380 GIF images. Various experimental results demonstrate the efficacy of our techniques.