Incorporating Deep Visual Features into Multiobjective based Multi-view Search Results Clustering

Sayantan Mitra, Mohammed Hasanuzzaman, Sriparna Saha, Andy Way

Abstract
Current paper explores the use of multi-view learning for search result clustering. A web-snippet can be represented using multiple views. Apart from textual view cued by both the semantic and syntactic information, a complimentary view extracted from images contained in the web-snippets is also utilized in the current framework. A single consensus partitioning is finally obtained after consulting these two individual views by the deployment of a multiobjective based clustering technique. Several objective functions including the values of a cluster quality measure measuring the goodness of partitionings obtained using different views and an agreement-disagreement index, quantifying the amount of oneness among multiple views in generating partitionings are optimized simultaneously using AMOSA. In order to detect the number of clusters automatically, concepts of variable length solutions and a vast range of permutation operators are introduced in the clustering process. Finally, a set of alternative partitioning are obtained on the final Pareto front by the proposed multi-view based multiobjective technique. Experimental results by the proposed approach on several benchmark test datasets of SRC with respect to different performance metrics evidently establish the power of visual and text-based views in achieving better search result clustering.
Anthology ID:
C18-1321
Volume:
Proceedings of the 27th International Conference on Computational Linguistics
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3793–3805
Language:
URL:
https://aclanthology.org/C18-1321
DOI:
Bibkey:
Cite (ACL):
Sayantan Mitra, Mohammed Hasanuzzaman, Sriparna Saha, and Andy Way. 2018. Incorporating Deep Visual Features into Multiobjective based Multi-view Search Results Clustering. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3793–3805, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
Cite (Informal):
Incorporating Deep Visual Features into Multiobjective based Multi-view Search Results Clustering (Mitra et al., COLING 2018)
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https://preview.aclanthology.org/remove-xml-comments/C18-1321.pdf
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