Data and Model-Driven Selection Using Color Regions
International Journal of Computer Vision
Local operators to detect regions of interest
Pattern Recognition Letters - special issue on pattern recognition in practice V
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image classification using the frequencies of simple features
Pattern Recognition Letters
Unsupervised Multiresolution Segmentation for Images with Low Depth of Field
IEEE Transactions on Pattern Analysis and Machine Intelligence
Saliency, Scale and Image Description
International Journal of Computer Vision
Automatic Identification of Perceptually Important Regions in an Image
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
A Method for Detecting Artificial Objects in Natural Environments
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Multiobjective design of operators that detect points of interest in images
Proceedings of the 10th annual conference on Genetic and evolutionary computation
GREC'09 Proceedings of the 8th international conference on Graphics recognition: achievements, challenges, and evolution
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In this paper, we shall address the issue of semantic extraction of different regions of interest. The proposed approach is based on statistical methods and models inspired from linguistic analysis. Here, the models used are Zipf law and inverse Zipf law. They are used to model the frequency of appearance of the patterns contained in images as power law distributions. The use of these models allows to characterize the structural complexity of image textures. This complexity measure indicates a perceptually salient region in the image. The image is first partitioned into sub-images that are to be compared in some sense. Zipf or inverse Zipf law are applied to these sub-images and they are classified according to the characteristics of the power law models involved. The classification method consists in representing the characteristics of the Zipf and inverse Zipf model of each sub-image by a point in a representation space in which a clustering process is performed. Our method allows detection of regions of interest which are consistent with human perception, inverse Zipf law is particularly significant. This method has good performances compared to more classical detection methods. Alternatively, a neural network can be used for the classification phase.