The Research Of Object Classification And Recognition Problem Based On Incomplete Feature Information

Object classification and identification is an important research topic in the fields of image processing and pattern recognition, and it has been widely used in computer vision like scene view and intelligent navigation. According to the two key problems of feature extraction and object classification, on the basis of layering and sorting the features, selecting the optimal feature permutation and combination, the paper proposes a fast layering decision algorithm based on attribute discriminatory power which can classify and recognize the objects under the incomplete information condition. Experimental results show that this method greatly improves the running efficiency of program and satisfies the real-time and accuracy requirements.The specific work and innovations are as follows:Firstly, according to the incomplete and inaccurate phenomenon of object information presentation, the paper introduces the thought of evolutionary vision object recognition mechanism which can supplement the correct information under the condition of multi-replication, multi-angle and multi-period and discard the false and retain the true. Thereby, the accuracy and fineness of object interpretation can be improved.Secondly, according to the existence of redundancy and irrelevance between each feature in machine learning and pattern recognition and the storage cost increment problem of data size, on the basis of researching the feature selection algorithms of Fisher criterion and hierarchical clustering, the paper proposes a feature selection and sorting algorithm, which can figure out the attribute separating capacity between any object combination.Thirdly, according to the problem of slow speed and low accuracy of classification and identification caused by unreasonable utilization of object features, on the basis of classification and recognition method of Bayes information fusion, the paper proposes a fast layering recognition algorithm based on attribute discriminatory power, which fuses the information in the case of incomplete features after sorting the attributes. Compared with the Naive Bayes and common hierarchy recognition method, the recognition rate and accuracy rate of the algorithm has been greatly improved.Fourthly, through two typical application examples, the paper segments the scene image and recognizes the road area on the basis of reasoning the process of object layering recognition. The experimental results show that the algorithm proposed in the paper has a better performance and largely improves the object classification and recognition efficiency, which will have reference value to the research of object classification in various scenes and interested target recognition problem.At last, the whole paper is summarized and further follow-up working thoughts are raised.