The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
International Journal of Computer Vision
Photobook: content-based manipulation of image databases
International Journal of Computer Vision
VisualSEEk: a fully automated content-based image query system
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Combining supervised learning with color correlograms for content-based image retrieval
MULTIMEDIA '97 Proceedings of the fifth ACM international conference on Multimedia
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Image Indexing Using Color Correlograms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Histogram refinement for content-based image retrieval
WACV '96 Proceedings of the 3rd IEEE Workshop on Applications of Computer Vision (WACV '96)
Spatial Color Indexing and Applications
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Video Retrieval Method Using Non-parametric Based Motion Classification
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
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In this paper we propose a novel approach for image retrieval based on edge structural features using edge correlogram and color coherence vector. After color vector angle is applied in the pre-processing stage, an image is divided into two image parts (high frequency image and low frequency image). In a low frequency image, the global color distribution of smooth pixels is extracted by color coherence vector, and thereby spatial information is incorporated into the proposed color descriptor. Meanwhile, in a high frequency image, the distribution of the gray pairs at an edge is extracted by edge correlogram. Since the proposed algorithm includes the spatial and edge information between colors, it can robustly reduce the effect of the significant change in appearance and shape of objects. The proposed method provides a simple and flexible description for the image with complex scene in terms of structural features from the image contents. Experimental evidence shows that our algorithm outperforms the recent histogram refinement methods for image indexing and retrieval. To index the multi-dimensional feature vectors, we use R*-tree structure.