Analysis And Research Of Content-Based Image Retrieval Technical

In the traditional approach of content-based image retrieval, the wide image domain results in the wide semantic gap between the low-level features and the high-level concepts. So image enhancement technology is used Bayesian framework content-based sensation grouping image retrieval: After image enhancement technology processing image gloomy degree and its the color light and shade enhance after image enhancement technology processing, and draws the image color characteristic through sensation grouping to carry on Bayesian classification, and carries on the retrieval according to Lx a x b x space colored range estimation condition. Confirms after the experiment, this retrieval effect must be better than the ordinary method. Another, directly collection of the original format of image retrieval is more. As the volume of data, storage or transmission brings inconvenience . Virtues of based on wavelet transform and binary mode of image retrieval lie in solving a large volume of data, omission decompress link, eigenvector included in the compressed domain search foreign coefficient;on the other hand binary mode of image is more in favor of the extraction of image texture and shape features. Experimental results showed that combining the two methods can improve the retrieval efficiency. At the same time, there are two inherent drawbacks in the traditional K-means algorithm:(1) For the initial random selection it may lead to different clustering results, even without the presence of the situation;(2) The algorithm is based on the gradient descent algorithm, it is inevitable that a partial often excellent. These two deficiencies greatly limit the scope of its application.Based on PSO of K-means clustering algorithm is in the traditional clustering algorithm introduced in the PSO algorithm. Theoretical analysis and experimental results show that the clustering algorithm to overcome the traditional clustering algorithm existing problems, global optimization capability is superior to the existing genetic algorithm based on the k-means clustering algorithm, and has a faster convergence rate.Finally,we have compared the experimental results with histogram in six color space and blocking weighted HSV color histogram, gray concurrence matrix and invariant moments were developed to extract texture and shape features in image retrieva1.With the relevance feedback mechanism,users could not only control the retrieval procedure,but also obtained satisfying retrieval results by adjusting relevance weights.The experiment shows that the proposed algorithm is effective and can achieve better results.