International Journal of Computer
Trends and Technology

Research Article | Open Access | Download PDF

Volume 6 | Number 2 | Year 2013 | Article Id. IJCTT-V6N5P141 | DOI : https://doi.org/10.14445/22312803/IJCTT-V6N5P141

Image Search Reranking

V Rajakumar , Vipeen V Bopche

Citation :

V Rajakumar , Vipeen V Bopche, "Image Search Reranking," International Journal of Computer Trends and Technology (IJCTT), vol. 6, no. 2, pp. 242-247, 2013. Crossref, https://doi.org/10.14445/22312803/IJCTT-V6N5P141

Abstract

The existing methods for image search re ranking suffer from the unfaithfulness of the assumptions under which the text-based images search result. The resulting images contain more irrelevant images. Hence the re ranking concept arises to re rank the retrieved images based on the text around the image and data of data of image and visual feature of image. A number of methods are differentiated for this re-ranking. The high-ranked images are used as noisy data and a k-means algorithm for classification is learned to rectify the ranking further. We are study the affectability of the cross validation method to this training data. The preeminent originality of the overall method is in collecting text/metadata of image and visual features in order to achieve an automatic ranking of the images. Supervision is initiated to learn the model weights offline, previous to reranking process. While model learning needs manual labeling of the results for a some limited queries, the resulting model is query autonomous and therefore applicable to any other query .Examples are given for a selection of other classes like vehicles, animals and other classes.

Keywords

vulnerability (or) security flows, vulnerability discovery Attack Injection, attack generator, proactive Protocol.

References

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