A Survey On Semantic Image Retrieval For Bigdata

Ms.N.N. Deepika, Dr. R. Madhumitha
<span title="2017-03-19">2017</span> <i title="Valley International"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/7itxqhdltnewtn4ueymnlhnkea" style="color: black;">International Journal Of Engineering And Computer Science</a> </i> &nbsp;
From last few years, there is an enormous growth in size of digital image collections. Every day, millions of images are being generated, in that semantic Image retrieval is the most complex process in the real time scenario where the similarity finding would be more difficult in case of larger homogeneous image contents. In Semantic based image retrieval, the weight adjustment scheme is used to give the high priority for the contents which are semantically more related. However, the weight
more &raquo; ... stment scheme doesn't care about the user feedback which might reduce the user satisfaction level. In this survey, homogeneous image data is retrieved by the proposed fuzzy logic based feedback weight adjustment scheme which will increase the score of the class which is more preferred by the users.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.18535/ijecs/v6i3.21">doi:10.18535/ijecs/v6i3.21</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/worc74lj6vas7i2dh4vbrctfo4">fatcat:worc74lj6vas7i2dh4vbrctfo4</a> </span>
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