A Survey on Annotation using Natural Language Vocabulary in CBIR
Gurdeep Kaur1, Gaganpreet Kaur2, Sushil Garg3

1Gurdeep Kaur, CSE, Desh Bhagat University, Mandi Gobindgarh, India.
2Gaganpreet Kaur, CSE,RimtEngg College, Mandi Gobindgarh, India.
3Sushil Garg, HODCSE, Rimt Engg College, Mandi Gobindgarh, India.
Manuscript received on March 11, 2013. | Revised Manuscript Received on March 12, 2013. | Manuscript published on March 25, 2013. | PP: 56-58 | Volume-1 Issue-5, March 2013. | Retrieval Number: E0227031513/2013©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Efforts to reduce semantic gaps in CBIR is an ongoing process, numerous models have been proposed to reduce this gap, each one of these models has some advantages and some limitations. Increase in the volume of multimedia repository has further complicated the design and development of an appropriate model which can help in eliminating the semantic gap [3]. This paper examines the problems that may arise as a result of semantic gap, various models which are presently available for reducing the semantic gap [3].
Keywords: Fast Image Searching in Huge databases (FISH), fuzzydataset, Fuzzy support vector machine (FSVM), manualannotation, region of interest,semantic.