Artificial Intelligence
Visual information retrieval from large distributed online repositories
Communications of the ACM
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
NeTra: a toolbox for navigating large image databases
Multimedia Systems - Special issue on video content based retrieval
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Content-Based Image and Video Retrieval
Content-Based Image and Video Retrieval
Modern Information Retrieval
MUSE: A Content-Based Image Search and Retrieval System Using Relevance Feedback
Multimedia Tools and Applications
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
Benchmarking for Content-Based Visual Information Search
VISUAL '00 Proceedings of the 4th International Conference on Advances in Visual Information Systems
Image Matching Using the OBIR System with Feature Point Histograms
VDB4 Proceedings of the IFIP TC2/WG 2.6 Fourth Working Conference on Visual Database Systems 4
Attentional Selection for Object Recognition A Gentle Way
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
An Attention-Based Approach to Content-Based Image Retrieval
BT Technology Journal
Attentional mechanisms for interactive image exploration
EURASIP Journal on Applied Signal Processing
Towards a comprehensive survey of the semantic gap in visual image retrieval
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Implementing the expert object recognition pathway
ICVS'03 Proceedings of the 3rd international conference on Computer vision systems
Is bottom-up attention useful for object recognition?
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Object-based image retrieval using the statistical structure of images
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Narrowing the semantic gap - improved text-based web document retrieval using visual features
IEEE Transactions on Multimedia
CLUE: cluster-based retrieval of images by unsupervised learning
IEEE Transactions on Image Processing
An unsupervised method for clustering images based on their salient regions of interest
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Using visual attention to extract regions of interest in the context of image retrieval
Proceedings of the 44th annual Southeast regional conference
A smart automatic thumbnail cropping based on attention driven regions of interest extraction
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
A parallel analysis on scale invariant feature transform (SIFT) algorithm
APPT'11 Proceedings of the 9th international conference on Advanced parallel processing technologies
Dynamic saliency models and human attention: a comparative study on videos
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
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Recent work in the computational modeling of visual attention has demonstrated that a purely bottom-up approach to identifying salient regions within an image can be successfully applied to diverse and practical problems from target recognition to the placement of advertisement. This paper proposes an application of a combination of computational models of visual attention to the image retrieval problem. We demonstrate that certain shortcomings of existing content-based image retrieval solutions can be addressed by implementing a biologically motivated, unsupervised way of grouping together images whose salient regions of interest (ROIs) are perceptually similar regardless of the visual contents of other (less relevant) parts of the image. We propose a model in which only the salient regions of an image are encoded as ROIs whose features are then compared against previously seen ROIs and assigned cluster membership accordingly. Experimental results show that the proposed approach works well for several combinations of feature extraction techniques and clustering algorithms, suggesting a promising avenue for future improvements, such as the addition of a top-down component and the inclusion of a relevance feedback mechanism.