What is the goal of sensory coding?
Neural Computation
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Distance measures for PCA-based face recognition
Pattern Recognition Letters
Robust Object Recognition with Cortex-Like Mechanisms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical statistical models of computation in the visual cortex
Hierarchical statistical models of computation in the visual cortex
Object Class Recognition and Localization Using Sparse Features with Limited Receptive Fields
International Journal of Computer Vision
Investigating visual feature extraction methods for image annotation
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Texture segmentation based on neuronal activation degree of visual model
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
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Semantic image annotation can be viewed as a mapping procedure from image features to semantic labels, by the steps of image feature extraction and image-semantic mapping. The features can be low-level visual features, such as color, texture, shape, etc., and the semantic labels can be related to the knowledge of human on the image understanding. However, these linear representations are insufficient to describe the complex natural scene. In this paper, we study currently existing visual models that are able to imitate the way the human visual system acts for the tasks of object recognition and scene interpretation. Therefore, it is expected to bring a better understanding to the image visual content in human cortex will. In the experiments, there are three state-of-the-art visual models are investigated for the application of automatic image annotation. The results demonstrate that with our proposed strategy, the annotation accuracy is improved comparing to the most used low-level linear representation features.