Modeling visual attention via selective tuning
Artificial Intelligence - Special volume on computer vision
Digital image processing
Active object recognition integrating attention and viewpoint control
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A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
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Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Metric for Distributions with Applications to Image Databases
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Learning-based Approach for Annotating Large On-Line Image Collection
MMM '04 Proceedings of the 10th International Multimedia Modelling Conference
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Pattern Recognition
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Pattern Recognition
Is bottom-up attention useful for object recognition?
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Joint semantics and feature based image retrieval using relevance feedback
IEEE Transactions on Multimedia
IEEE Transactions on Multimedia
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IEEE Transactions on Image Processing
An efficient and effective region-based image retrieval framework
IEEE Transactions on Image Processing
Detection of visual attention regions in images using robust subspace analysis
Journal of Visual Communication and Image Representation
An efficient algorithm for attention-driven image interpretation from segments
Pattern Recognition
Searching satellite imagery with integrated measures
Pattern Recognition
Eye movement data modeling using a genetic algorithm
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Tree structures with attentive objects for image classification using a neural network
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Content-based image retrieval using a combination of visual features and eye tracking data
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Where are focused places of a photo?
VISUAL'07 Proceedings of the 9th international conference on Advances in visual information systems
Extracting salient visual attention regions by color contrast and wavelet transformation
ISCIT'09 Proceedings of the 9th international conference on Communications and information technologies
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ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Salient region detection by jointly modeling distinctness and redundancy of image content
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Expert Systems with Applications: An International Journal
An attention based similarity measure for colour images
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
A novel image retrieval method based on mutual information descriptors
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
Semantic context based refinement for news video annotation
Multimedia Tools and Applications
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Visual attention, a selective procedure of human's early vision, plays a very important role for humans to understand a scene by intuitively emphasizing some focused regions/objects. Being aware of this, we propose an attention-driven image interpretation method that pops out visual attentive objects from an image iteratively by maximizing a global attention function. In this method, an image can be interpreted as containing several perceptually attended objects as well as a background, where each object has an attention value. The attention values of attentive objectives are then mapped to importance factors so as to facilitate the subsequent image retrieval. An attention-driven matching algorithm is proposed in this paper based on a retrieval strategy emphasizing attended objects. Experiments on 7376 Hemera color images annotated by keywords show that the retrieval results from our attention-driven approach compare favorably with conventional methods, especially when the important objects are seriously concealed by the irrelevant background.