“School of Cognitive Sciences”
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| Paper IPM / Cognitive Sciences / 13435 |
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| Abstract: | |||||
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In recent studies on computational vision from the eï¬cient coding viewpoint, it assumed that visual sensory neurons are adjusted to the statistical properties of natural environment during sensory evolution. Thus understanding the statistics of natural images can help us to comprehend the function of visual sensory processing and perception. Natural images have non-random structures that reï¬ect causal diï¬erences in the world. However, decorrelated natural images contain obvious structures but many of the important forms in the natural images require higher-order statistics to describe. It has been demonstrated that higher-order statistical structures of images are basis for the visual perception and object recognition and investigating such regularities could assist to clarify the spatiotemporal function of neurons in V1 and beyond. Nonetheless, higher-order statistics properties of natural scenes and their representation in the neural population are still unclear. In this study we extract spectra features from natural vs non-natural images by using statistical methods to construct an statistical model of natural image space. Our ï¬ndings indicate that there are some signiï¬cant diï¬erences between natural images and random spaces which are critical for visual perception. Our results will be useful for optimal modeling of visual system and can help to develop hierarchical models for learning non-linear regularities in natural images.
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