• GANNBATHULLA VEERAVENKATA RAMANA BABU
• V.SARALA

Heterogeneous web media consists of many visual concepts, such as objects, scenes and activities, that cannot be semantically decomposed. The task of learning fundamental visual concepts (FVCs) plays an important role in automatically understanding the elements that compose all visual media, as well as in applications of retrieval, annotation, etc.In this project we try to apply annotations for all the images which are uploaded in the online web by taking neighboring concept distributing (NCD) attribute ,in which those which are having some similar difference will be added with same type of annotation and then they are tagged.

FUNDAMENTAL VISUAL CONCEPT LEARNING FROM CORRELATED IMAGES AND TEXT ANNOTATION

"FUNDAMENTAL VISUAL CONCEPT LEARNING FROM CORRELATED IMAGES AND TEXT ANNOTATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 7, page no.g486-g492, July-2021, Available :http://www.jetir.org/papers/JETIR2107797.pdf

"FUNDAMENTAL VISUAL CONCEPT LEARNING FROM CORRELATED IMAGES AND TEXT ANNOTATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 7, page no. ppg486-g492, July-2021, Available at : http://www.jetir.org/papers/JETIR2107797.pdf