Iconic indexing by 2-D strings
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Efficient and effective querying by image content
Journal of Intelligent Information Systems - Special issue: advances in visual information management systems
Design and evaluation of algorithms for image retrieval by spatial similarity
ACM Transactions on Information Systems (TOIS)
Saliency, Scale and Image Description
International Journal of Computer Vision
Image Indexing Using Color Correlograms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Spatio-temporal shape contexts for human action retrieval
IMCE '09 Proceedings of the 1st international workshop on Interactive multimedia for consumer electronics
Wavelet-based salient region extraction
PCM'07 Proceedings of the multimedia 8th Pacific Rim conference on Advances in multimedia information processing
Salient region filtering for background subtraction
VISUAL'07 Proceedings of the 9th international conference on Advances in visual information systems
A generalized coding artifacts and noise removal algorithm for digitally compressed video signals
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part I
Multi-spectral saliency detection
Pattern Recognition Letters
Tag-Saliency: Combining bottom-up and top-down information for saliency detection
Computer Vision and Image Understanding
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In this paper, we present an image retrieval technique for specific objects based on salient regions. The salient regions we select are invariant to geometric and photometric variations. Those salient regions are detected based on low level features, and need to be classified into different types before they can be applied on further vision tasks. We first classify the selected regions into four types including blobs, edges and lines, textures, and texture boundaries, by using the correlations with the neigbouring regions. Then, some specific region types are chosen for further object retrieval applications. We observe that regions selected from images of the same object are more similar to each other than regions selected from images of different objects. Correlation is used as the similarity measure between regions selected from different images. Two images are considered to contain the same object, if some regions selected from the first image are highly correlated to some regions selected from the second image. Two data sets are employed for experiment: the first data set contains human face images of a number of different people and is used for testing the retrieval algorithm on distinguishing specific objects of the same category; and the second data set contains images of different objects and is used for testing the retrieval algorithm on distinguishing objects of different categories. The results show that our method is very effective on specific object retrieval.