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PWIS: Personalized Web Image Search using One-Click Method
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 130 - Number 10 |
| Year of Publication: 2015 |
| Authors: Pushpa C.N., Vinay Kumar N.K., Thriveni J., Venugopal K.R. |
10.5120/ijca2015907101
|
Pushpa C.N., Vinay Kumar N.K., Thriveni J., Venugopal K.R. . PWIS: Personalized Web Image Search using One-Click Method. International Journal of Computer Applications. 130, 10 ( November 2015), 39-47. DOI=10.5120/ijca2015907101
Abstract
Personalized Web Image search is the one searching for the particular images of User intention on the Web. For searching images, a user might provide query terms like keyword, image file, or click on few image file, and therefore the system can determine the images similar to the query. The similarity used for search criteria could be Meta tags, color distribution in images, region/shapes attributes, etc. Web-scale image search engines namely Google and Bing searches for images are relying on the surrounding text features. It is highly cumbersome and complicated for the web-scale based image search engines to interpret users search intention only by querying of keywords. This leads to the incorporation of noise and high ambiguity in the search results which are extremely unfit in the context of the users. It's also a necessary mandate for using visual information for solving the problem of ambiguity in the text-based image retrieval scenario. In the case of Google search, search text box will auto complete while user is typing similar added keywords. This method will differ from user intention while searching. So to avoid this kind of faults, it is important to use visual information in order to solve the uncertainty in text-based image retrieval. To retrieve exact matching, and acquire user’s intention we can allow them text query with extended or related images as a suggestion. We have proposed an innovative Web image search approach. It only needs the user to click on one query image with minimal effort and images from a pool fetched by text-based search are re-ranked based on both visual and textual contents.
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