Multi-modal Clustering for Multimedia Collections

Bekkerman, Ron; Jeon, Jiwoon

doi:10.1109/cvpr.2007.383223

Cited by 60 publications

(57 citation statements)

References 15 publications

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“…In this subsection, we evaluate the overall complexity of CoStar and compare it to the complexity of competitors used in Section 5 (SRC [23], NMF [6] and ComRaf [3]). …”

Section: Overall Complexitymentioning

confidence: 99%

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Parameter-less co-clustering for star-structured heterogeneous data

Ienco

Robardet

Pensa

et al. 2012

Data Min Knowl Disc

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The availability of data represented with multiple features coming from heterogeneous domains is getting more and more common in real world applications. Such data represent objects of a certain type, connected to other types of data, the features, so that the overall data schema forms a star structure of inter-relationships. Co-clustering these data involves the specification of many parameters, such as the number of clusters for the object dimension and for all the features domains. In this paper we present a novel co-clustering algorithm for heterogeneous star-structured data that is parameter-less. This means that it does not require either the number of row clusters or the number of column clusters for the given feature spaces. Our approach optimizes the Goodman-Kruskal's τ , a measure for cross-association in contingency tables that evaluates the strength of the relationship between two categorical variables. We extend τ to evaluate co-clustering solutions and in particular we apply it in a higher dimensional setting. We propose the algorithm CoStar which optimizes τ by a local search approach. We assess the performance of CoStar on publicly available datasets from the textual and image domains using objective external criteria. The results show that our approach outperforms state-of-the-art methods for the co-clustering of heterogeneous data, while it remains computationally efficient.

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“…In this subsection, we evaluate the overall complexity of CoStar and compare it to the complexity of competitors used in Section 5 (SRC [23], NMF [6] and ComRaf [3]). …”

Section: Overall Complexitymentioning

confidence: 99%

“…A Monte Carlo simulation method is used to learn the parameters and to assign objects to clusters. The problem of clustering images described by segments 5 and captions is considered in [3]. The proposed algorithm is based on Markov random fields in which some of the nodes are random variables in the combinatorial problem.…”

Section: Related Workmentioning

confidence: 99%

Parameter-less co-clustering for star-structured heterogeneous data

Ienco

Robardet

Pensa

et al. 2012

Data Min Knowl Disc

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“…This can be used as privileged information in object classification task. We use IsraelImages dataset introduced in [2]. The dataset has 11 Table 2: ImageNet dataset, group of snakes (bounding box annotation as privileged information).…”

Section: Textual Description As Privileged Informationmentioning

confidence: 99%

“…We first train an ordinary ranking SVM on X * . The resulting ranking function f * we use to compute the margins achieved between any two training images 2 ,…”

Section: Rank Transfermentioning

confidence: 99%

Learning to Rank Using Privileged Information

Sharmanska

Quadrianto

Lampert

2013

2013 IEEE International Conference on Computer Vision

152

153

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“…Multi-modal clustering has received much attention during the past decade [4,5]. However, most methods assume a complete bipartite mapping between modalities.…”

Section: Introductionmentioning

confidence: 99%

Multiple-view constrained clustering for unsupervised face identification in TV-broadcast

Bendris

Favre

Charlet

et al. 2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Our goal is to automatically identify faces in TV broadcast without a pre-defined dictionary of identities. Most methods are based on identity detection (from OCR and ASR) and require a propagation strategy based on visual clustering. In TV content, people appear with many variations making the clustering difficult. In this case, speaker clustering can be a reliable link for face clustering. We propose in this paper to build automatically an incomplete speaker-face mapping based on local evidence of OCR and Lip activity links. Then, we propose schemes of speaker constraints propagation to the face constrained-clustering problem. Experiments performed on the REPERE corpus show an improvement of face identification by propagating names to face clusters (+3.7% F-measure compared to the baseline).

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Multi-modal Clustering for Multimedia Collections

Abstract: Abstract

Cited by 60 publications

References 15 publications

Parameter-less co-clustering for star-structured heterogeneous data

Parameter-less co-clustering for star-structured heterogeneous data

Learning to Rank Using Privileged Information

Multiple-view constrained clustering for unsupervised face identification in TV-broadcast

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