Image Clustering Using Local Discriminant Models and Global Integration

Yang, Yi; Xu, Dong; Nie, Feiping; Yan, Shuicheng; Zhuang, Yueting

doi:10.1109/tip.2010.2049235

Cited by 345 publications

(248 citation statements)

References 27 publications

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“…2. For reference, we also show the corresponding timings for affinity propagation, a well-known modern clustering algorithm (10), and LDMGI, the baseline that demonstrated the best performance across datasets (48). Fig.…”

Section: Ami(cĉ)mentioning

confidence: 99%

Robust continuous clustering

Shah

Koltun²

2017

Proc. Natl. Acad. Sci. U.S.A.

156

106

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Clustering is a fundamental procedure in the analysis of scientific data. It is used ubiquitously across the sciences. Despite decades of research, existing clustering algorithms have limited effectiveness in high dimensions and often require tuning parameters for different domains and datasets. We present a clustering algorithm that achieves high accuracy across multiple domains and scales efficiently to high dimensions and large datasets. The presented algorithm optimizes a smooth continuous objective, which is based on robust statistics and allows heavily mixed clusters to be untangled. The continuous nature of the objective also allows clustering to be integrated as a module in end-to-end feature learning pipelines. We demonstrate this by extending the algorithm to perform joint clustering and dimensionality reduction by efficiently optimizing a continuous global objective. The presented approach is evaluated on large datasets of faces, handwritten digits, objects, newswire articles, sensor readings from the Space Shuttle, and protein expression levels. Our method achieves high accuracy across all datasets, outperforming the best prior algorithm by a factor of 3 in average rank.clustering | data analysis | unsupervised learning C lustering is one of the fundamental experimental procedures in data analysis. It is used in virtually all natural and social sciences and has played a central role in biology, astronomy, psychology, medicine, and chemistry. Data-clustering algorithms have been developed for more than half a century (1). Significant advances in the last two decades include spectral clustering (2-4), generalizations of classic center-based methods (5, 6), mixture models (7, 8), mean shift (9), affinity propagation (10), subspace clustering (11-13), nonparametric methods (14, 15), and feature selection (16)(17)(18)(19)(20).Despite these developments, no single algorithm has emerged to displace the k -means scheme and its variants (21). This is despite the known drawbacks of such center-based methods, including sensitivity to initialization, limited effectiveness in high-dimensional spaces, and the requirement that the number of clusters be set in advance. The endurance of these methods is in part due to their simplicity and in part due to difficulties associated with some of the new techniques, such as additional hyperparameters that need to be tuned, high computational cost, and varying effectiveness across domains. Consequently, scientists who analyze large high-dimensional datasets with unknown distribution must maintain and apply multiple different clustering algorithms in the hope that one will succeed. Books have been written to guide practitioners through the landscape of data-clustering techniques (22).We present a clustering algorithm that is fast, easy to use, and effective in high dimensions. The algorithm optimizes a clear continuous objective, using standard numerical methods that scale to massive datasets. The number of clusters need not be known in advance.The operation of the algorithm ...

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Section: Ami(cĉ)mentioning

confidence: 99%

Robust continuous clustering

Shah

Koltun²

2017

Proc. Natl. Acad. Sci. U.S.A.

156

106

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“…To address this issue, we follow the works in [22,23], where the indicated matrix is given. At first, we introduce the indicator matrix Y = {0, 1} N×K , where Y ij = 1 if the i-th data point belongs to the j-th group.…”

Section: Discriminative Constraintsmentioning

confidence: 99%

Group Sparsity and Graph Regularized Semi-Nonnegative Matrix Factorization with Discriminability for Data Representation

Luo

Peng

2017

Entropy

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Semi-Nonnegative Matrix Factorization (Semi-NMF), as a variant of NMF, inherits the merit of parts-based representation of NMF and possesses the ability to process mixed sign data, which has attracted extensive attention. However, standard Semi-NMF still suffers from the following limitations. First of all, Semi-NMF fits data in a Euclidean space, which ignores the geometrical structure in the data. What's more, Semi-NMF does not incorporate the discriminative information in the learned subspace. Last but not least, the learned basis in Semi-NMF is unnecessarily part based because there are no explicit constraints to ensure that the representation is part based. To settle these issues, in this paper, we propose a novel Semi-NMF algorithm, called Group sparsity and Graph regularized Semi-Nonnegative Matrix Factorization with Discriminability (GGSemi-NMFD) to overcome the aforementioned problems. GGSemi-NMFD adds the graph regularization term in Semi-NMF, which can well preserve the local geometrical information of the data space. To obtain the discriminative information, approximation orthogonal constraints are added in the learned subspace. In addition, 21 norm constraints are adopted for the basis matrix, which can encourage the basis matrix to be row sparse. Experimental results in six datasets demonstrate the effectiveness of the proposed algorithms.

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“…Various clustering methods including K-means, spectral clustering have demonstrated strong competency in handing non-linearly connected data [14,16], which are common in multi-label analysis of images. For the task of discovering Meta Object-groups for efficient image retrieval, literature finds that clustering by message passing [5] offers performance advantages in finding clusters based-on non-metric similarity measures.…”

Section: Related Workmentioning

confidence: 99%

Dimension reduction with meta object-groups for efficient image retrieval

et al. 2015

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Bag-of-Word (BoW) has been a prominent form for representing visual content such as image and video in recent years, as a result of its unique capability of characterizing visual content in a picture-level while still preserving part of the object-level information. However, it is also noticed that the dimensionality of BoW is usually as high as a few hundreds or even thousands, posing a serious challenge for any application that requires efficient processing. In this article we propose a dimension reduction method called Meta object-Group Component (MGC) to tackle this problem. MGC aims at discovering the hidden relations of objects by examining the correlations between dimensions in the BoW features and maximizing the relations of the members in a meta object-group. By exchanging message passing between object-groups, meta object-groups are identified for a dataset. A meta object-group does not only contain visually similar objects, but also includes objects that are likely to co-occur with each other. As the meta object groups are obtained, group-specific dimension reduction is performed to obtain denser representations for efficient retrieval. We evaluate the framework on the NUS-Wide image dataset with approximately 270,000 images represented by BoW features, and demonstrate its advantage over existing method.

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Image Clustering Using Local Discriminant Models and Global Integration

Cited by 345 publications

References 27 publications

Robust continuous clustering

Robust continuous clustering

Group Sparsity and Graph Regularized Semi-Nonnegative Matrix Factorization with Discriminability for Data Representation

Dimension reduction with meta object-groups for efficient image retrieval

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