A reinforcement learning model of selective visual attention
Proceedings of the fifth international conference on Autonomous agents
Content based image retrieval and information theroy: a general approach
Journal of the American Society for Information Science and Technology - Visual based retrieval systems and web mining
Informaton theoretic similarity measures for content based image retrieval
Journal of the American Society for Information Science and Technology - Visual based retrieval systems and web mining
IEEE Transactions on Pattern Analysis and Machine Intelligence
Saliency, Scale and Image Description
International Journal of Computer Vision
Histogram Preserving Image Transformations
International Journal of Computer Vision
A Coarse-to-Fine Deformable Contour Optimization Framework
IEEE Transactions on Pattern Analysis and Machine Intelligence
Resolution Selection Using Generalized Entropies of Multiresolution Histograms
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Tracking and Rendering Using Dynamic Textures on Geometric Structure from Motion
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
A Scale-Space Based Approach for Deformable Contour Optimization
SCALE-SPACE '99 Proceedings of the Second International Conference on Scale-Space Theories in Computer Vision
Multiresolution Histograms and Their Use for Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Selective visual attention enables learning and recognition of multiple objects in cluttered scenes
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
Evaluation of selective attention under similarity transformations
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
The representation and matching of categorical shape
Computer Vision and Image Understanding
Local Scale Measure for Remote Sensing Images
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
Salience in orientation-filter response measured as suspicious coincidence in natural images
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Selective visual attention enables learning and recognition of multiple objects in cluttered scenes
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
Evaluation of selective attention under similarity transformations
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
Real-time scale selection in hybrid multi-scale representations
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
Efficient image concept indexing by harmonic & arithmetic profiles entropy
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Motion and color analysis for animat perception
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Estimation of structural information content in images
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
Attentive object detection using an information theoretic saliency measure
WAPCV'04 Proceedings of the Second international conference on Attention and Performance in Computational Vision
A salience-based quality metric for visualization
EuroVis'10 Proceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization
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Information measures with respect to spatial locations and scales of objects in an image are important to image processing and interpretation. It allows us to focus attention on relevant data, saving effort and reducing false positives. In particular, the information content of a man-made scene is typically confined to a small set of scales. We devise a scale space based measure of image information. Kullback contrasts between successive resolution lengths gives the differential information gain. Experiments show that this measure gives a clear indication of characteristic lengths in a variety of real world images and is superior to power spectrum based measurements. Decomposing the expected information gain into spatial coordinates gives us a saliency map for use by an attention selector. We combine the scale and spatial decompositions into a single information measure, giving both the spatial extent and scale range of interest. The information measure has an efficient implementation, and thus can be used routinely in early vision processing.