Research And Application Of Image Recognition Method Base On Visual Feature Distribution

Image recognition is a typical field of pattern recognition and a new technology in the area of computer application. It uses computers for image processing, analysis and understanding, to identify a variety of different patterns of target objects. In this area, scene semantic classification and object recognition in the scene are popular research topics. Object recognition is for the object category problem and let the computers identify which object is in the scene and where it is; the scene classification is a very important research filed of scene description and understanding, and its goal is to give a set of semantic classes annotation, such as coast, mountains, forests, streets, etc., for the images in database, which plays an important role in guiding object identification and can provide effective context information for further understanding of other content in the scene image.This paper focuses on the problem of object recognition and scene classification, based on the extraction of images'color, texture, shape and local invariant feature, high-level semantic features are introduced, mainly the use of the spatial relationship between objects and relationship between object and the scene in the image, for image recognition. The understanding of the content of the image has been increased to the understanding of the image objects and their spatial relationship, and especially the influence of image block mechanism and the distribution of visual features in the image to classification effect is focus on.Specifically, at first a model named bag of spatial visual words is proposed, which is based on traditional bag model and spatial similarity of the image in same scene classification, and made significant recognition rate improvement in both scene classification and object recognition tasks. Similarly, based on the spatial similarity between images and content correlation between image blocks, A new scene classification method is presented, in which the noise blocks are filtered by PageRank algorithm firstly, and then prior knowledge is combined to determine the test image blocks'scene information, finally voting mechanism is used for semantics scene classification. The final recognition results achieved is good.Secondly, an importance assessment method of visual vocabulary is presented based on the analysis of the distribution of image features, which can reduce the dimension of image features histogram while improving classification accuracy. At the same time through the analysis of spatial distribution characteristics of Census Transform histograms and PACT, the implicit step edge template is introduced to spacial PACT feature, and the implementation efficiency of the algorithm is improve greatly with a equivalent recognition rate.Then, a sea ice monitoring system for measuring sea ice thickness, density and velocity and other parameters based on image recognition technology is designed and implemented, and a variety of image recognition solutions for solving practical problems are proposed. At the same time, we propose an acceleration strategy combined with CUDA parallel programming model and OpenMP parallel technology to accelerate the image feature extracting and matching process.Finally, through in-depth study of the image template matching strategy, we propose a template matching method based on fuzzy theory for recognizing design patterns from the source code and improve the matching accuracy effectively.