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
Multimedia Tools and Applications
Perceptual image hashing with histogram of color vector angles
AMT'12 Proceedings of the 8th international conference on Active Media Technology
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The need for tools that effectively filter and efficiently search through a large amount of visual data is on the increase due to the rapid growth of multimedia information. Towards this goal, 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 to an image in the pre-processing stage, it is divided into two parts, as either smooth or edge pixels by the pixel classification. For the smooth pixels, the global color distribution of pixels is extracted by color coherence vector, incorporating spatial information into the proposed color descriptor. Meanwhile, for the edge pixels, the distribution of the gray pairs at an edge is extracted by edge correlogram. As the proposed method has both information for the local spatial correlation and information of global distribution of colors, it can be used to reduce the effect of the significant change in appearance and shape of objects. From the image representation based on edge structural features, the proposed algorithm provides a concise and flexible description even for the image with the complicated scenes. Experimental evidence shows that our algorithm outperforms the recent histogram refinement methods for image indexing and retrieval.