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Image Reranking Using Multimodal Sparse Coding

Mohammadi Aiman1 , Ruksar Fatima2

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-1 , Page no. 277-282, Jan-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i1.277282

Online published on Jan 31, 2019

Copyright © Mohammadi Aiman, Ruksar Fatima . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Mohammadi Aiman, Ruksar Fatima, “Image Reranking Using Multimodal Sparse Coding,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.277-282, 2019.

MLA Style Citation: Mohammadi Aiman, Ruksar Fatima "Image Reranking Using Multimodal Sparse Coding." International Journal of Computer Sciences and Engineering 7.1 (2019): 277-282.

APA Style Citation: Mohammadi Aiman, Ruksar Fatima, (2019). Image Reranking Using Multimodal Sparse Coding. International Journal of Computer Sciences and Engineering, 7(1), 277-282.

BibTex Style Citation:
@article{Aiman_2019,
author = {Mohammadi Aiman, Ruksar Fatima},
title = {Image Reranking Using Multimodal Sparse Coding},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {1 2019},
volume = {7},
Issue = {1},
month = {1},
year = {2019},
issn = {2347-2693},
pages = {277-282},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=3497},
doi = {https://doi.org/10.26438/ijcse/v7i1.277282}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i1.277282}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=3497
TI - Image Reranking Using Multimodal Sparse Coding
T2 - International Journal of Computer Sciences and Engineering
AU - Mohammadi Aiman, Ruksar Fatima
PY - 2019
DA - 2019/01/31
PB - IJCSE, Indore, INDIA
SP - 277-282
IS - 1
VL - 7
SN - 2347-2693
ER -

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Abstract

Image reranking is effective for improving the performance of a text-based image search. However, existing reranking algorithms are limited for two main reasons: 1) the textual meta-data associated with images is often mismatched with their actual visual content and 2) the extracted visual features do not accurately describe the semantic similarities between images. Recently, user click information has been used in image reranking, because clicks have been shown to more accurately describe the relevance of retrieved images to search queries. However, a critical problem for click-based methods is the lack of click data, since only a small number of web images have actually been clicked on by users. Therefore, we aim to solve this problem by predicting image clicks. We propose a multimodal hypergraph learning-based sparse coding method for image click prediction, and apply the obtained click data to the reranking of images. We adopt a hypergraph to build a group of manifolds, which explore the complementarily of different features through a group of weights. Unlike a graph that has an edge between two vertices, a hyperedge in a hypergraph connects a set of vertices, and helps preserve the local smoothness of the constructed sparse codes. An alternating optimization procedure is then performed, and the weights of different modalities and the sparse codes are simultaneously obtained. Finally, a voting strategy is used to describe the predicted click as a binary event (click or no click), from the images’ corresponding sparse codes. Thorough empirical studies on a large-scale database including nearly 330K images demonstrate the effectiveness of our approach for click prediction when compared with several other methods. Additional image reranking experiments on real world data show the use of click prediction is beneficial to improving the performance of prominent graph-based image reranking algorithms.

Key-Words / Index Term

Image reranking, click, manifolds, sparse codes

References

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