Content based Image Processing using Relevance Feedback with Null Space LDA (NLDA)

The biggest problem in the research of Content Based Image Retrieval (CBIR) is bridge the gap between low-level features and high-level semantics. , Still many shortcomings for image retrieval system only with the low level visual features due to the semantic space. It is better for the relevance feedback based on the user involvement in image retrieval system. By using the help of user's feedback, the resultant high-level semantic will be obtained. Relevance feedback is a technique for incorporating semantic information in image retrieval. This paper illustrates a development upon a relevance feedback approach that utilizes semantic grouping and clustering technique to close the distance between low-level features and high-level semantics. Distinctively, the past system is improved by incorporating the images in the same group as the query image in the collection of retrieved images. Shared the retrieval results with relevance feedback technology, image feature dimensional reduction was prepared using the Clustering concepts. The given system reduces semantic gap and the storage of image signatures, and also improves the retrieval efficiency and performance. The result shows the efficiency of our proposed system.