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34
Scene segmentation using the wisdom of crowds
 In Proc. ECCV
, 2008
"... Abstract. Given a collection of images of a static scene taken by many different people, we identify and segment interesting objects. To solve this problem, we use the distribution of images in the collection along with a new fieldofview cue, which leverages the observation that people tend to tak ..."
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Cited by 12 (4 self)
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Abstract. Given a collection of images of a static scene taken by many different people, we identify and segment interesting objects. To solve this problem, we use the distribution of images in the collection along with a new fieldofview cue, which leverages the observation that people tend to take photos that frame an object of interest within the field of view. Hence, image features that appear together in many images are likely to be part of the same object. We evaluate the effectiveness of this cue by comparing the segmentations computed by our method against handlabeled ones for several different models. We also show how the results of our segmentations can be used to highlight important objects in the scene and label them using noisy userspecified textual tag data. These methods are demonstrated on photos of several popular tourist sites downloaded from the Internet. 1
Which Data Sets are ‘Clusterable’? – A Theoretical Study of Clusterability
"... We investigate measures of the clusterability of data sets. Namely, ways to define how ‘strong ’ or ‘conclusive ’ is the clustering structure of a given data set. We address this issue with generality, aiming for conclusions that apply regardless of any particular clustering algorithm or any specifi ..."
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Cited by 7 (0 self)
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We investigate measures of the clusterability of data sets. Namely, ways to define how ‘strong ’ or ‘conclusive ’ is the clustering structure of a given data set. We address this issue with generality, aiming for conclusions that apply regardless of any particular clustering algorithm or any specific data generation model. We survey several notions of clusterability that have been discussed in the literature, as well as propose a new notion of data clusterability. Our comparison of these notions reveals that, although they all attempt to evaluate the same intuitive property, they are pairwise inconsistent. Our analysis discovers an interesting phenomenon; the more clusterable a data set is, the easier it is (computationally) to find a closetooptimal clustering of that data. It has been recently shown that such a property holds with respect to one notion of clusterability, ‘k separability’. We show that this phenomenon holds for other notions as well. In particular, we prove that for well clusterable data, using the clusterability notions we discuss, nearoptimal clustering can be efficiently computed. Finally, we investigate how hard it is to determine the clusterability value of a given data set. In most cases, it turns out that this is an NPhard problem. 1.
Community structure in online collegiate social networks
, 2008
"... Abstract. We study the structure of social networks of students by examining the graphs of Facebook “friendships ” at five American universities at a single point in time. We investigate each singleinstitution network’s community structure and employ graphical and quantitative tools, including stan ..."
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Cited by 7 (1 self)
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Abstract. We study the structure of social networks of students by examining the graphs of Facebook “friendships ” at five American universities at a single point in time. We investigate each singleinstitution network’s community structure and employ graphical and quantitative tools, including standardized paircounting methods, to measure the correlations between the network communities and a set of selfidentified user characteristics (residence, class year, major, and high school). We review the basic properties and statistics of the employed paircounting indices and recall, in simplified notation, a useful analytical formula for the zscore of the Rand coefficient. Our study illustrates how to examine different instances of social networks constructed in similar environments, emphasizes the array of social forces that combine to form “communities, ” and leads to comparative observations about online social lives that can be used to infer comparisons about offline social structures.
Multiscale Modularity in Complex Networks
, 1004
"... Abstract—We focus on the detection of communities in multiscale networks, namely networks made of different levels of organization and in which modules exist at different scales. It is first shown that methods based on modularity are not appropriate to uncover modules in empirical networks, mainly ..."
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Cited by 7 (1 self)
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Abstract—We focus on the detection of communities in multiscale networks, namely networks made of different levels of organization and in which modules exist at different scales. It is first shown that methods based on modularity are not appropriate to uncover modules in empirical networks, mainly because modularity optimization has an intrinsic bias towards partitions having a characteristic number of modules which might not be compatible with the modular organization of the system. We argue for the use of more flexible quality functions incorporating a resolution parameter that allows us to reveal the natural scales of the system. Different types of multiresolution quality functions are described and unified by looking at the partitioning problem from a dynamical viewpoint. Finally, significant values of the resolution parameter are selected by using complementary measures of robustness of the uncovered partitions. The methods are illustrated on a benchmark and an empirical network. Index Terms—community detection, complex networks, modularity, multiscale. I.
The NVI Clustering Evaluation Measure
"... Clustering is crucial for many NLP tasks and applications. However, evaluating the results of a clustering algorithm is hard. In this paper we focus on the evaluation setting in which a gold standard solution is available. We discuss two existing information theory based measures, V and VI, and show ..."
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Cited by 5 (0 self)
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Clustering is crucial for many NLP tasks and applications. However, evaluating the results of a clustering algorithm is hard. In this paper we focus on the evaluation setting in which a gold standard solution is available. We discuss two existing information theory based measures, V and VI, and show that they are both hard to use when comparing the performance of different algorithms and different datasets. The V measure favors solutions having a large number of clusters, while the range of scores given by VI depends on the size of the dataset. We present a new measure, NVI, which normalizes VI to address the latter problem. We demonstrate the superiority of NVI in a large experiment involving an important NLP application, grammar induction, using real corpus data in English, German and Chinese. 1
A General Purpose ComputerAssisted Clustering Methodology: Supplemental Notes
, 2010
"... We summarize here the types of different clustering algorithms included in our applications and software. Existing algorithms are most often described as either statistical and algorithmic. The statistical models are primarily mixture models, including a large variety of finite mixture models (Frale ..."
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Cited by 5 (2 self)
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We summarize here the types of different clustering algorithms included in our applications and software. Existing algorithms are most often described as either statistical and algorithmic. The statistical models are primarily mixture models, including a large variety of finite mixture models (Fraley and Raftery, 2002; Banerjee et al., 2005; Quinn et al., 2006), infinite mixture models based on the Dirichlet process prior (Blei and Jordan, 2006), and mixture models (Blei, Ng and Jordan, 2003). The algorithmic approaches include methods that partition the documents directly, those that create a hierarchy of clusterings, and those which add an additional step to the clustering procedure. The methods include some which identify an exemplar document for each cluster (Kaufman and Rousseeuw, 1990; Frey and Dueck, 2007) and those which do not (Schrodt and Gerner, 1997; Shi and Malik, 2000; Ng, Jordan and Weiss, 2002; von Luxburg, 2007). The hierarchical methods can be further subdivided into agglomerative (Hastie, Tibshirani and Friedman, 2001), divisive (Kaufman and Rousseeuw, 1990), and other hybrid methods (Gan, Ma and Wu, 2007). To use in our program, we obtain a flat partition of the documents from hierachical clustering methods. A final group includes methods which group words and documents together simulatenously (Dhillon, 2003) and those which embed the documents into lower dimensional space and then cluster (Kohonen,
Globally optimal closedsurface segmentation for connectomics. ECCV
, 2012
"... Abstract. We address the problem of partitioning a volume image into a previously unknown number of segments, based on a likelihood of merging adjacent supervoxels. Towards this goal, we adapt a higherorder probabilistic graphical model that makes the duality between supervoxels and their joint fac ..."
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Cited by 4 (3 self)
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Abstract. We address the problem of partitioning a volume image into a previously unknown number of segments, based on a likelihood of merging adjacent supervoxels. Towards this goal, we adapt a higherorder probabilistic graphical model that makes the duality between supervoxels and their joint faces explicit and ensures that merging decisions are consistent and surfaces of final segments are closed. First, we propose a practical cuttingplane approach to solve the MAP inference problem to global optimality despite its NPhardness. Second, we apply this approach to challenging largescale 3D segmentation problems for neural circuit reconstruction (Connectomics), demonstrating the advantage of this higherorder model over independent decisions and finiteorder approximations. 1
Local equivalence of distances between clusterings
, 2009
"... In comparing clusterings, several different distances and indices are in use. We prove that the Misclassification Error distance, the Hamming distance (equivalent to the unadjusted Rand index), and the dχ2 distance between partitions are equivalent in the neighborhood of 0. In other words, if two pa ..."
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Cited by 4 (1 self)
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In comparing clusterings, several different distances and indices are in use. We prove that the Misclassification Error distance, the Hamming distance (equivalent to the unadjusted Rand index), and the dχ2 distance between partitions are equivalent in the neighborhood of 0. In other words, if two partitions are very similar, then one distance defines upper and lower bounds on the other and viceversa. The proof is geometric and relies on the convexity of a certain set of probability measures. To my knowledge, this is the first result of its kind. The motivation for this work is in the area of data clustering. Practically, these distances are frequently used to compare two clusterings of a set of observations. Theoretically, such distances are involved in formulating and proving properties of clusterig algorithms. Besides, our results apply to any pair of finite valued random variables, and provides simple yet tight upper and lower bounds on the χ 2 measure of (in)dependence valid when the two variables are strongly dependent. 1 Motivation Clustering, or finding partitions in data, has become an increasingly popular part of data
Improved Unsupervised POS Induction through Prototype Discovery
"... We present a novel fully unsupervised algorithm for POS induction from plain text, motivated by the cognitive notion of prototypes. The algorithm first identifies landmark clusters of words, serving as the cores of the induced POS categories. The rest of the words are subsequently mapped to these cl ..."
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Cited by 3 (0 self)
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We present a novel fully unsupervised algorithm for POS induction from plain text, motivated by the cognitive notion of prototypes. The algorithm first identifies landmark clusters of words, serving as the cores of the induced POS categories. The rest of the words are subsequently mapped to these clusters. We utilize morphological and distributional representations computed in a fully unsupervised manner. We evaluate our algorithm on English and German, achieving the best reported results for this task. 1
Type Level Clustering Evaluation: New Measures and a POS Induction Case Study
"... Clustering is a central technique in NLP. Consequently, clustering evaluation is of great importance. Many clustering algorithms are evaluated by their success in tagging corpus tokens. In this paper we discuss type level evaluation, which reflects class membership only and is independent of the tok ..."
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Clustering is a central technique in NLP. Consequently, clustering evaluation is of great importance. Many clustering algorithms are evaluated by their success in tagging corpus tokens. In this paper we discuss type level evaluation, which reflects class membership only and is independent of the token statistics of a particular reference corpus. Type level evaluation casts light on the merits of algorithms, and for some applications is a more natural measure of the algorithm’s quality. We propose new type level evaluation measures that, contrary to existing measures, are applicable when items are polysemous, the common case in NLP. We demonstrate the benefits of our measures using a detailed case study, POS induction. We experiment with seven leading algorithms, obtaining useful insights and showing that token and type level measures can weakly or even negatively correlate, which underscores the fact that these two approaches reveal different aspects of clustering quality. 1