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Research Of Visual Attention Model And Its Application On Biologically Inspired Object Recognition

Posted on:2011-11-11Degree:DoctorType:Dissertation
Country:ChinaCandidate:J XiaoFull Text:PDF
GTID:1118330332967969Subject:Pattern Recognition and Intelligent Systems
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The primate visual system employs an attention mechanism to limit processing to important information that is currently relevant to behaviors or visual tasks. It can efficiently deal with the balance between computing resources, time cost and performing different visual tasks in a normal, cluttered and dynamic environment. The application of visual attention mechanism in computational model can assign the finite computation resources to more important tasks. There exist two ways by which information can be used to direct attention, bottom-up, image-based saliency cues and top-down, task-dependent guidance cues. How to use the two kind of cues efficiently, guide attention to target-relevant regions promptly and serve for object recognition perfectly, is of great significance. Based on the theory on neuroscience, pattern recognition and image processing, the biological visual attention procedures are deeply analyzed. The visual attention mechanism and its application on object search and recognition are developed. In summary, the following main works have been accomplished in this dissertation.Development of a new approach of visual attended region extraction. A new model of region extraction is proposed based on saliency-based region selection and scale-space primal sketch. For a input color image, the extent of object is estimated by means of saliency-based region selection, which considers feature that contributes most to the saliency map in bottom-up visual attention model. After that, the color image is changed into gray-level image and the local maxima on each scale are computed. The blob of largest response is picked, which is in the same area with the salient region obtained from the previous step, and then these spatial regions are combined together. The segmentation obtained is coarse in the sense that the localization of object boundaries may not be rigid. However, the segmentation is safe in the manner that those regions can be served as attended regions which extremely reduce the data redundancy.Development of a new visual attention model based on object-accumulation visual mechanism. From the research on visual attended region extraction, blobs can be reckoned as the reflection of important structure in scale-space. Therefore, the information of blob feature can guide perceptual grouping and lead the attention to task-relevant regions. By introducing multi-level blobs and connecting blob properties and low-level features in our model, the knowledge representations for prior information can be built by blob features. For any new given scene, the proposed model can use the prior knowledge to render th object more salient by enhancing their features which are characteristic of the object, then recursively group regions together to form objects, guided by blob features extracted from the intermediate data computed at pre-attention stage. Selective visual attention in the proposed model can be effectively directed to task-relevant regions. The comparison of the proposed model against other attention models proved its superiority.Development of a model for object searching and recognition based on object-accumulation visual attention mechanism. For the effective description of object and forming the top-down information, an automatic object learning approach based on object-accumulation mechanism is proposed. The approach can reuse the data in current visual attention framework to represent target object, produce accumulation strategy, and output the object representation vector. Accordingly an object search and recognition approach based on object-accumulation mechanism is proposed. The object representation vector served as top-down information can be combined with bottom-up information from image. Taking into account blob feature extracted from multi-scale set of low-level feature maps, the model recursively combines regions to form objects, promptly guide the attention to search relevant object, fully extract object region, and provide primary recognition result. The proposed model acquired 88.5%recognition rate which proved its efficiency.Development of a novel method for evaluating how well the attended regions contribute to the recognition of the target based on sift algorithm. At present, there are some problems existed in visual attention model. In one hand, the model cannot fully utilize the bottom-up information in image and intermediate data produced in the pre-attention stage. As for complex scene, there exists huge gap between the computation efficiency of the computional model and the perception efficiency of the biological visual system. In the other hand, the way how to introduce the prior information is not plausible, the general model for good adaptation cannot be acquired. The consequence is that the attended region extracted by computational model cannot have a comprehensive coverage of the target. Better coverage of target region for attention can better serve for recognition. Based on SIFT recognition algorithm, a novel evaluation approach is proposed to achieve an objective validity description instead of judging by people subjectively in previous works. Firstly, SIFT features have been extracted from a reference image and stored in a database as object learning in advance. For attended regions extracted by different visual attention models, the algorithm computes the SIFT features in the region and compares them with the keypoints stored for each object in the database. Secondly, a formula is defined to compute the validity of the attended region based on the accuracy of the fit of the SIFT keypoints and probable number of region SIFT keypoints. The comparison of the proposed model with classic evaluation criterion recall-precision demonstrated its superiority.
Keywords/Search Tags:selective visual attention, attended region, blob feature, object accumulation, perceptual grouping, SIFT, object recognition, remote sensing image
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