Dissertation
Dissertation > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device

Integration of Spatial Information Bag of Feature in Image Annotation

Author ChangFeng
Tutor TaoWenBing
School Huazhong University of Science and Technology
Course Computer Applications
Keywords Target recognition Conditional Random Fields Contextual information Image Segmentation Graph cuts Energy Optimization Word bag model
CLC TP391.41
Type Master's thesis
Year 2011
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Image automatic target recognition marked an important problem in computer vision research , has been great development in recent years . The word bag model based on SIFT (Scale-invariant feature transform) the characteristics of the key points in the image scene classification and object recognition marked a good application . But there are still many problems , the scale transform light conversion , multi-angle changes , the difference between the same categories as well as the increase in the category of identification labeling caused great difficulties . For the traditional automatic target recognition marked by the presence of some of the problems in the following aspects have made ??some of the innovative work : 1) extracted based on a priori user interactive target framework to expand to two types of interactive image segmentation problem multi- class situation . The combination of multi-layered graph model , the use of the user's initial brush global energy function optimization , image segmentation results . 2 ) based on the dictionary space neighborhood characteristics , describing the image feature , taking into account the the word bag model ignores the structural relationship of the image space , the local spatial relationship of the image into a feature vector , improvement of image-based key point identification method , and this method is applied to a scene classification , achieved better recognition results . 3) integration of image pre-processing technology based on multi- partition and regional neighborhood histogram statistics , combined with the optimized conditions with the airport , to study the use of guide target identification problem of image segmentation , and then combined with top-down and bottom-up learning methods, to identify and locate the image of the target. Finally , by a plurality of classification vote decisions final result of the identification . Automatic image annotation systems and interactive object extraction system , given an image pattern recognition and machine learning methods , automatic annotation of the presence of the target and its location in the image , and reached more in some mainstream data set high recognition rate .

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