An Integrated Methods for Refined Facial-Image Retrieval using Sift and Click-Through Data
Mr. C. Balasubramanian1, J. Raja Sekar2
1C. Balasubramanian, Department of Computer Science and Engineering, Mepco Schlenk Engineering College, Sivakasi (Tamil Nadu), India.
2Dr. J. Raja Sekar, Department of Computer Science and Engineering, Mepco Schlenk Engineering College, Sivakasi (Tamil Nadu), India.
Manuscript received on 15 December 2019 | Revised Manuscript received on 22 December 2019 | Manuscript Published on 31 December 2019 | PP: 48-51 | Volume-9 Issue-1S6 December 2019 | Retrieval Number: A10111291S619/19©BEIESP | DOI: 10.35940/ijeat.A1011.1291S619
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© The Authors. 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: In the modern era, the emergence of social networking sites paved the way to the people to upload lot of images online. Social sites like Instagram and Flickr allow users to add semantic information to the images in the form of tags. Often these tags are the firsthand semantic data for retrieving the images from the Internet. When a user searches for images in the web, the images with tags relevant to user query are retrieved. Most of the time, these semantic data are not relevant to the content of the image and hence the user gets irrelevant images in contrast to their intended search. This is more common in facial image search. In this paper, we propose an integrated approach to refine the relevance of retrieved facial images. Using Scale Invariant Feature Extraction (SIFT) the facial features of semantically most relevant image and click-through data of all the retrieved images are used to rank and present a meaningful search result. Along with facial features and click through information, the co-occurrence of related tags is also considered. Also we propose the construction of inverted index structure to improve the search performance.
Keywords: Facial Images, Inverted Index, SIFT, Click-Through Data, Co-Occurrence.
Scope of the Article: Data Analytics