Intelligent pattern searching method

Abstract

The invention discloses an intelligent graphic retrieval method, which is characterized in that: extracting features of graphics to generate feature set by the method of Fourier change, training RBF neural network classification model by taking one part of the feature set as training set, indexing the graphics using the classification result given by the classification model; client of a retrieval system extracts the features of retrieval graphics, gives a category by the trained classification model and computes the similarity distance between the retrieval graphics and each graphics in the feature set of the same category; sorting the similarity distance, returning to the graphics according to the number made by the system and further revising RBF neural network classification model by relevant feedback methods. The invention improves the intelligence of search process, effectively determines the RBF neural network classification model by improved algorithm of subtractive clustering, greatly improves retrieval precision, speeds up retrieval speed and upgrades retrieval performance.

Description

Technical field: [0001] The invention relates to a graphic retrieval method, in particular to an intelligent graphic retrieval method. Background technique: [0002] Graphic retrieval has broad application prospects in many fields such as computer-aided design, molecular biology, medicine, chemistry, automobile manufacturing and industrial manufacturing. Similarity matching based on the content contained in the graph itself is an effective retrieval method. It is necessary to extract the feature vector of the graph with the feature abstraction method, and convert the similarity matching of the graph into the nearest neighbor search between the feature vectors. Graphic similarity retrieval needs to consider two important factors: speed and precision. [0003] The traditional similarity retrieval method is to retrieve the eigenvectors of the model and all the eigenvectors in the feature library to calculate the similarity distance, and find the nearest k. The traditional simi...

AI Technical Summary

Problems solved by technology

[0006] The technical problem to be solved by the present invention is to improve the management effectiveness and retrieval efficiency of the graphics library, and provide a classification model to train the graphics library and then perform category indexing on the graphics library, which considers the relatively time-consuming problem of retrieval , replace the retrieval of the entire graphics library with the retrieval of the same category of graphics, which ensures the rapidity of the retrieval; and further corrects the classification model through the method of relevant feedback, avoiding the unsatisfactory retrieval results caused by the incorrect classification of the retrieval objects question

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