• P. Anandababu
• M. Kamarasan

Presently, the massive growth in the generation of digital images has initiated the requirement for the enhancement in the way of identifying and retrieving images from a huge dataset. A major issue exist in the process is the way to retrieve the essential images from a huge dataset with high precision with less amount of retrieval time. The choice of proper features acts as a vital part in the enhancement of the results of image retrieval models. Numerous image retrievals have been presented and exist in the literature. In this paper, a detailed comparative study between two image retrieval models namely Fused Information Feature (FIF) and color, texture and edge features (CTE) based image retrieval model. To analyze the better retrieval performance of these two models, a detailed experimentation takes place on the benchmark Corel 10-K dataset. The experimental analysis ensured that the FIF model is found to be an efficient model which is proved by attaining a maximum average precision value of 83.33% whereas the CTE model achieves slightly lower performance by obtaining an average precision value of 82.22%

CBIR; Corel 10K, Image Retrieval; Precision

"Performance Analysis of Efficient Content Based Image Retrieval Frameworks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 7, page no.1-6, July 2019, Available :http://www.jetir.org/papers/JETIRDE06001.pdf

"Performance Analysis of Efficient Content Based Image Retrieval Frameworks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 7, page no. pp1-6, July 2019, Available at : http://www.jetir.org/papers/JETIRDE06001.pdf