An introduction to genetic algorithms
An introduction to genetic algorithms
Task-dependent learning of attention
Neural Networks
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Evaluation of visual attention models under 2D similarity transformations
Proceedings of the 2009 ACM symposium on Applied Computing
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 2
Evaluation of selective attention under similarity transformations
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
Attentive object detection using an information theoretic saliency measure
WAPCV'04 Proceedings of the Second international conference on Attention and Performance in Computational Vision
Attentional Landmarks and Active Gaze Control for Visual SLAM
IEEE Transactions on Robotics
Cue-guided search: a computational model of selective attention
IEEE Transactions on Neural Networks
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This paper proposes a new method for content-based image retrieval that uses a computational model of visual attention and genetic algorithm to find a given object in a set of images with different backgrounds. This method is composed by three main modules: a visual attention model that is quite robust against affine transformations; a color-based schematic representation of visual information; and a genetic algorithm that optimizes several parameters of the visual attention model in order to focus the attention mechanism on those regions of the image where it is most likely that a given object is present. The proposed method is validated through several experiments, and these experiments show that it can find the images that contain the sought object as well as the position and scale of the object in these images.