Images have become a standard for information consumption and storage, far replacing text in various domains such as museums, news stations, medicine and remote sensing. Such images constitute of the majority of data being consumed on the Internet today and the volume is constantly increasing day by day. Most of these images are unlabeled and devoid of any keywords. The swift and continuous increase in the use of images and their unlabeled characteristics have demanded the need for efficient and accurate content-based image retrieval systems. A considerable number of such systems have been designed for the task that derive features from a query image and show the most similar images. One such efficient and accurate system is attempted in this work which makes use of color and texture information of the images and retrieves the best possible results based on this information. The proposed method makes use of Color Coherence Vector (CCV) for color feature extraction and Gabor Filters for texture features. The results were found to be significantly higher and easily exceeded a few popular studies as well.
Dataset (1000 images) taken from: http://wang.ist.psu.edu/docs/related/
Mean Precision = 89% (for top 10 images retrieved)
This project was done in a group as Major Project for B.E. under the guidance of Dr. Jyotsna Singh. It was also published as a research paper at: https://ieeexplore.ieee.org/document/8692123