A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge-based structural features for content-based image retrieval
Pattern Recognition Letters - Special issue on image/video indexing and retrieval
Unsupervised Segmentation of Color-Texture Regions in Images and Video
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SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
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Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
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Object-based visual attention for computer vision
Artificial Intelligence
Contrast-based image attention analysis by using fuzzy growing
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Salient Closed Boundary Extraction with Ratio Contour
IEEE Transactions on Pattern Analysis and Machine Intelligence
Localized content based image retrieval
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Attention-driven image interpretation with application to image retrieval
Pattern Recognition
A survey of content-based image retrieval with high-level semantics
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Image retrieval: Ideas, influences, and trends of the new age
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A novel fusion approach to content-based image retrieval
Pattern Recognition
A novel region-based image retrieval algorithm using selective visual attention model
ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
A Dynamic User Concept Pattern Learning Framework for Content-Based Image Retrieval
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Thresholding using two-dimensional histogram and fuzzy entropy principle
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An efficient and effective region-based image retrieval framework
IEEE Transactions on Image Processing
Unsupervised extraction of visual attention objects in color images
IEEE Transactions on Circuits and Systems for Video Technology
Proceedings of the ACM International Conference on Image and Video Retrieval
Rigid registration of renal perfusion images using a neurobiology-based visual saliency model
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Hierarchical Salient Point Selection for image retrieval
Pattern Recognition Letters
Color image segmentation based on regional saliency
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
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Selective visual attention plays an important role for humans to understand an image by intuitively emphasizing some salient parts. Such mechanism can be well applied in localized content-based image retrieval, due to the fact that in the context of CBIR, the user is only interested in a portion of the image and the rest of the image is irrelevant. Being aware of this, in this paper, the selective visual attention model (SVAM) is incorporated in the CBIR task to estimate the user's retrieval concept. In contrast with existing learning based retrieval algorithms which need relevance feedback strategy to get user's high-level semantic information, the proposed method does not need any user's interaction to provide the training data. From this point of view, our method can be regarded as the purely bottom-up manner while learning based algorithms belong to the top-down manner. Specifically, an improved saliency map computing algorithm is employed first. Then, based on the saliency map, an efficient salient edges and regions detection method is introduced. Moreover, the concepts of salient edge histogram descriptors (SEHDs) and salient region adjacency graphs (SRAGs) are proposed, respectively, for images' similarity comparison. Finally, an integrated strategy is adopted for content-based image retrieval. Experiments show that the proposed algorithm can characterize the human perception well and achieve satisfying retrieval performance.