• Savita
• Brijesh vishwakarma

The retrieval principle of CBIR systems is based on visual features such as colour, texture, and shape or the semantic meaning of the images. To enhance the retrieval speed, most CBIR systems pre-process the images stored in the database. This is because feature extraction algorithms are often computationally expensive. If images are to be retrieved from the World-Wide-Web (WWW), the raw images have to be downloaded and processed in real time. In this case, the feature extraction speed becomes crucial. Ideally, systems should only use those feature extraction algorithms that are most suited for analyzing the visual features that capture the common relationship between the images in hand. In this thesis, a statistical discriminate analysis based feature selection framework is proposed. Such a framework is able to select the most appropriate visual feature extraction algorithms by using relevance feedback only on the user labeled samples. The idea is that a smaller image sample group is used to analyze the appropriateness of each visual feature, and only the selected features will be used for image comparison and ranking. As the number of features is less, an improvement in the speed of retrieval is achieved.

CBIR, Histogram, RGB, Digital Image

"Improve Web Mining Search Engine using 3d Histogram", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.663-666, June 2019, Available :http://www.jetir.org/papers/JETIR1906J31.pdf

"Improve Web Mining Search Engine using 3d Histogram", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp663-666, June 2019, Available at : http://www.jetir.org/papers/JETIR1906J31.pdf