Research On Image Retrieval Algorithm Based On Learning Clustering

With the development of information technology swiftly, data images become more and more popular. How to get the interest image from much image quickly is a key problem. Cluster analysis develops quickly, and is applied to artificial intelligence, information control, medicine diagnose, weather forecast, mage analysis and so on. The c-means cluster is most popular in all clusters.The c-means cluster is applied image analysis, but it has much shortcoming. Based on that, feedback is added into cluster, so that cluster can learn self. This cluster based on feedback is called learning cluster. Currently, three are two main image retrieval methods. One is based on content, another is based on semantic. Both have different merit and shortcoming, and have the same difficulties. BFSR(Based on Feature and Semantic Retrieval Algorithm) is brought forward, which combines traits both retrieval methods and is based on learning cluster. It has much merits, like figuring out initial center choose, label semantic automatically, depicting image content in many points of view. But it has a lot of shortcoming, like bad precision, semantic synonymy and bifurcation. Based on which, RFCL(Based on Relevant Feedback Cluster Learning Algorithm) is brought forward, it increases precision of result effectively.An experiment system is developed based on RFCL. The result reveals that RFCL can clear up synonymy and bifurcation of the semantic, and increases nicety of result via analysis recall and precision.