Title

Scene Modeling Using Co-Clustering

Abstract

In this paper, we propose a novel approach for scene modeling. The proposed method is able to automatically discover the intermediate semantic concepts. We utilize Maximization of Mutual Information (MMI) co-clustering approach to discover clusters of semantic concepts, which we call intermediate concepts. Each intermediate concept corresponds to a cluster of visterms in the Bag of Vis-terms (BOV) paradigm for scene classification. MMI co-clustering results in fewer but meaningful clusters. Unlike k-means which is used to cluster image patches based on their appearances in BOV, MMI co-clustering can group the visterms which are highly correlated to some concept. Unlike probabilistic Latent Semantic Analysis (pLSA), which can be considered as one-sided soft clustering, MMI co-clustering simultaneously clusters visterms and images, so it is able to boost both clustering. In addition, the MMI co-clustering is an unsupervised method. We have extensively tested our proposed approach on two challenging datasets: the fifteen scene categories and the LSCOM dataset, and promising results are obtained. ©2007 IEEE.

Publication Date

12-1-2007

Publication Title

Proceedings of the IEEE International Conference on Computer Vision

Number of Pages

-

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ICCV.2007.4408866

Socpus ID

50649105931 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/50649105931

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