Natural Languauge Fuzzy Set Based CBIR
Gurdeep Kaur1, Gaganpreet Kaur2, Sushil Garg3
1Gurdeep Kaur, CSE, Desh Bhagatuniversity, Mandi Gobindgarh, India.
2Gaganpreet kaur, CSE, RimtEngg College, Mandi Gobindgarh, India.
3Sushil Garg, CSE, Rimt Engg College, Mandi Gobindgarh, India.
Manuscript received on March 04, 2013. | Revised Manuscript Received on March 11, 2013. | Manuscript published on March 20, 2013. | PP: 15-17 | Volume-1 Issue-4, March 2013. | Retrieval Number: D0175031413/2013©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: Not much is understood in terms of semantics of natural language applicable in CBIR. There are many linguistic systems, often based on set theory and logic, attempting to grasp (at least some phenomena and circumstances) of the natural language based on which a image might understood or interpreted and thereby be annotated with , and it must useful in such way that it reduces the cost of accessing the relevant information and remains refresh all together with open ended structure to incorporate the validity of ground truths. In this research work we have tried to come all these issues and results shows that our method used in building natural language sets for images in question is highly relevant thus enriching results to provide high recall and precision value of range of 67% .
Keywords: fuzzy dataset, linguistic, raw symbolism, semantic.