Content Based Image Retrieval is an interesting topic of research since years. Specifically, it is on developing technologies for bridging the semantic gap that currently prevents wide-deployment of image content-based search engines. Image search engines currently in use are mostly rely on human generated data, such as text. Annotation of an image is totally depend on the person's perception who is going to store it into database. It is time-consuming as well as error prone. Therefore search engine using text input results in various non-relevant images. To overcome drawbacks of text based image retrieval, Content based image retrieval is introduced where retrieval of images is totally depend on the features of images. Mostly, content-based methods are based on low-level descriptions, while high-level or semantic descriptions are beyond current capabilities. In this paper, we will try to implement the technique to fill this gap. This technique can eventually be extended to allow for content-based similarity type of search, such as find similar or "query-by- example". When it comes to image retrieval, we have taken into account a very primary feature of the signal namely content. This feature is used as parameter for comparison and retrieval from the previously stored image databases.

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Image retrieval CBIR MBTC Kekre’s pattern