Multimodal Image Retrieval Based on Query Weighting

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Author(s):
Ebrahim Naderi, Elham Nikookar, Ali Tayebi Babookani
Published Date:
December 05, 2015
Issue:
Volume 5, Issue 2
Page(s):
1 - 6
Views:
2367
Downloads:
84

Keywords:
content-based image retrieval, learning to rank, multimodal retrieval, query weighting, ranking model, resampling, text-based image retrieval
Citation:
Ebrahim Naderi, Elham Nikookar, Ali Tayebi Babookani, "Multimodal Image Retrieval Based on Query Weighting". International Journal of Research in Computer Science, 5 (2): pp. 1-6, December 2015. Other Formats

Abstract

Image retrieval methods try to increase the performance of retrieval by adjusting weights of relevant and non-relevant images. Adjusting weights of queries instead of images is studied in this paper to see whether it increases or decreases performance of retrieval system. Evaluation measure of the method is calculated for each query instead of each sample. In proposed method, textual and visual features of images are used to retrieve ranked results. To train visual module, query weighting and updating weights in each iteration are used to concentrate on the queries which did not provide acceptable results and also cause performance drop of final retrieval. Compared with other previous researches, the results of applying the proposed method to IAPR TC-12 dataset indicate high efficiency of proposed method. Image retrieval methods like this can be used in a lot of applications such as, gasometer, digit recognition using gasometer, digits image etc.

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