BibTeX:

@article{IJIRSTV3I4133,
     title={Content-Based Image Retrieval using Feature Extraction and K-Means Clustering},
     author={Geethu Varghese and Dr. Arun Kumar M N},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={3},
     number={4},
     pages={291--302},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV3I4133.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}

There are many methods to retrieve an image from an amassment of images in the database in order to meet users demand with image content kindred attribute, edge pattern homogeneous attribute, color homogeneous attribute, etc. An image retrieval system offers an efficient way to access or retrieve set of similar images by directly computing the image features from images by directly computing the image features from an image as reported by utilizing different kinds of techniques as well as algorithm. Content based image retrieval (CBIR) is most recently used technique for image retrieval from large image database. The reason behind content based image retrieval is to get perfect and fast result. There are many technique of CBIR utilized for image retrieval. A Block Truncation Coding technique is the famous method used for image retrieval. In the proposed system the advanced technique of BTC is used that is Ordered Dither Block Truncation Coding (ODBTC). ODBTC encoded data stream to construct the image features namely Color Co-occurrence and Bit Pattern features. After the extraction of this feature similarity distance is computed for retrieving a set of similar images. And to make the search more accurate K-means clustering method is used. The most similar images to the query image are selected and these features are being appended together and k-means clustering is applied. This method retrieves more similar images to the query image than the first search. The proposed scheme can be considered as very good in color image retrieval application. The process is implemented in a MATLAB 2014.

Content-based image retrieval, bit pattern feature, color co-occurrence, K-means clustering, ordered dither block Truncation coding