Image mining methodologies for content based retrieval

Abstract

The thesis presents a system for content based image retrieval and mining. The research presents a design of a scalable solution for efficient retrieval of images from large image databases using image features such as color, shape and texture. A framework for automatic labeling of images and clustering of meta data in database based on the dominant shapes, textures and colors in the image is proposed. The thesis also presents a new image tagging methodology to annotate the dominant image features to the image as meta data.

The users of this system can input a query image and select similar image retrieval criteria by selecting a feature type from amongst color, texture or shape. The system retrieves images from the database that match the specified pattern and displays them by relevance. The user can enter a set of keywords or a combination of keywords that form the input text query. Images in the database that match the input text query are fetched and displayed. This ensures content based similar image search even for text based search. An efficient clustering algorithm is shown to improve the image retrieval by an order of magnitude.