Research On Image Recognition Inspired By Biological Vision

Image recognition is one of the hotspots in the field of vision research. Itsfundamental task is to categorize the image scene or identify the objects in the scene bythe computer. It has wide application prospects in the field of content-based imageretrieval, intelligent environment perception, military target recognition, etc.Conventional engineering techniques could perform well in most visual recognitiontasks of structural scenes. However, when it comes to the issues about non-structuralscene categorization and object recognition, it will be very difficult to get satisfactoryresults using these traditional methods. There are many problems to be solved, such as:how to weaken or eliminate the influences of uncertain factors in the environment, suchas noise, illumination, occlusion, etc., to achieve stable perception of natural images;how to capture the scene gist effectively to achieve fast scene perception andclassification; how to incorporate visual attention mechanism into image recognition toimprove object recognition performance, etc.Inspired by the visual perception mechanism of human and primates, and in viewof the physiological properties of visual cortex and relevant conclusions of cognitivepsychology, we make some exploring study on several issues mentioned above ofnatural image recognition. The main work of this dissertation is as follows:Pre-attentive boundary and surface perception: we work over how to detect theboundary contours of natural images robustly, and how to stably generate surfacelightness percepts under variable illumination conditions. The problems of BCS/FCSneural model proposed by Grossberg in processing natural images are analyzed, andcorresponding modification and optimization schemes are proposed. As a result, wecould detect the contours of natural images, which is robust to noise and small occlusion.Also, the modified lightness perception model could effectively generate absolutelightness percepts, which is insensitive to illuminations and overcomes the problems oforiginal model such as lightness information loss, fogging, and blurring. Besides, anovel image diffusion algorithm based on visual masking effect is proposed inspired bythe neuron activity diffusion mechanism in surface recovering and cognitive psychology.The proposed algorithm could effectively smooth noises while preserving importantstructural properties of the image.Fast perception of scene images: we propose a novel visual descriptor for scenerecognition. It is a holistic representation and could capture the global structuralproperties and rough geometrical information of the scene. The proposed method isconsistent with the psychophysical findings, which suggests that human could quicklyget the scene gist and percept scene categories through the spatial layout information ofthe scenes. Experimental results show that it performs better than classical SIFT descriptor in recognizing scene categories. It is very easy to implement and could becomputed very fast.Modeling visual spatial attention mechanism: we put forward a computationalmodel of visual attention motivated by cognitive psychology and neurophysiology. First,the input image is transformed into a psychovisual space. Then we construct afully-connected graph on each feature map. A random walk is adopted on each sub-bandgraph to simulate the information transmission among the neurons in visual cortex.Consequently, we derive the activity maps corresponding to every feature map from theinformation maximization principle. We obtain the final saliency map by summing upall the activity maps according to Feature-Integrated-Theory. The proposed visualattention model performs better than existing models in detecting region of interest andpredicting human fixations. In addition, the scene gist information could guide theselective attention of objects in the scene. So we present a contextual guidance model ofspatial attention to introduce the top-down modulating influences. The gist informationis obtained through the proposed global image representation mentioned above. Thecontextual guidance model of attention predicts the image regions likely to be fixated byhuman observers well in active visual search tasks.Object recognition incorporating visual attention mechanism: we build aframework for object recognition inspired by saccade-based visual memory, namely,NIMART. This framework incorporates visual attention into object recognition.NIMART simulates the sequential visual attention of fixating salient locations whenobservers learn and recognize objects in a scene. The saliency map derived from visualattention model is used to guide eye movements. Inhibition of return has beenconsidered in the sequential fixations. We analyze the fixated regions for learning anddecision-making with adaptive resonance theory (ART). NIMART accords with themechanism of learning and recognizing objects of the brain. Experiments demonstratethat it could perform very well on widely-used datasets.