• Ms.Jyoti B Thorat
• Prof .S.S.Bere

First introduce MoK-SimRank to significantly improve the speed and one of the most popular algorithms for computing node similarity in information networks. Next, propose an algorithm called SimLearn to (1) Extend MoK-SimRank to heterogeneous image-rich information network. (2 Both link-based and content based similarity accounts integrating reinforcement learning with feature learning.for that present a system called iRIN designed for performing image retrieval in image-rich information networks. Then, propose an algorithm Integrated Weighted Similarity Learning (IWSL) to account for both link-based and content based similarities by considering the network structure and mutually reinforcing link similarity . Then conduct experiments on Flickr and Amazon networks. The results achieves better performance than traditional approaches and Implement a new product search and recommendation system to find both visually semantically relevant products based on algorithm

Image Retrieval ,Information Network, ranking, Image Mining

"Survey of Heterogeneous Sim-Rank System For Image Intensional Search", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.3, Issue 2, page no.1-5, February-2016, Available :http://www.jetir.org/papers/JETIR1602001.pdf

"Survey of Heterogeneous Sim-Rank System For Image Intensional Search", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.3, Issue 2, page no. pp1-5, February-2016, Available at : http://www.jetir.org/papers/JETIR1602001.pdf