Weighted K-Means for Density-Biased Clustering

Kerdprasop, Kittisak; Kerdprasop, Nittaya; Sattayatham, Pairote

doi:10.1007/11546849_48

Cited by 49 publications

(24 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This clustering approach, however, is sensitive to the non-uniformity or bias in the dataset [21]. This is especially problematic for the K-means algorithm, since the calculation of a cluster center can be skewed by a few data points that are far away from the actual cluster centers [11,12].…”

Section: Distinctive Visual Vocabulary Generationmentioning

confidence: 98%

Image matching with distinctive visual vocabulary

Kang

Hebert

Kanade

2011

2011 IEEE Workshop on Applications of Computer Vision (WACV)

View full text Add to dashboard Cite

In this paper we propose an image indexing and matching algorithm that relies on selecting distinctive high dimensional features. In contrast with conventional techniques that treated all features equally, we claim that one can benefit significantly from focusing on distinctive features. We propose a bag-of-words algorithm that combines the feature distinctiveness in visual vocabulary generation. Our approach compares favorably with the state of the art in image matching tasks on the University of Kentucky Recognition Benchmark dataset and on an indoor localization dataset. We also show that our approach scales up more gracefully on a large scale Flickr dataset.

show abstract

Section: Distinctive Visual Vocabulary Generationmentioning

confidence: 98%

Image matching with distinctive visual vocabulary

Kang

Hebert

Kanade

2011

2011 IEEE Workshop on Applications of Computer Vision (WACV)

View full text Add to dashboard Cite

show abstract

“…For instance, in the weighted K-means algorithm (Figure 2), when the virtual point of a bucket with n tuples is inserted into a cluster, the centroid of the cluster is updated as if n identical points are inserted. Note that such simple weighted K-means extension has been used in various data mining and machine learning applications [17,21], although the "weight" in their situation has different meaning.…”

Section: Summary-based Clusteringmentioning

confidence: 99%

Supporting ranking and clustering as generalized order-by and group-by

Wang

Lim

et al. 2007

Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data

View full text Add to dashboard Cite

The Boolean semantics of SQL queries cannot adequately capture the "fuzzy" preferences and "soft" criteria required in non-traditional data retrieval applications. One way to solve this problem is to add a flavor of "information retrieval" into database queries by allowing fuzzy query conditions and flexibly supporting grouping and ranking of the query results within the DBMS engine. While ranking is already supported by all major commercial DBMSs natively, support of flexibly grouping is still very limited (i.e., group-by).In this paper, we propose to generalize group-by to enable flexible grouping (clustering specifically) of the query results. Different from clustering in data mining applications, our focus is on supporting efficient clustering of Boolean results generated at query time. Moreover, we propose to integrate ranking and clustering with Boolean conditions, forming a new type of ClusterRank query to allow structured data retrieval. Such an integration is nontrivial in terms of both semantics and query processing. We investigate various semantics of this type of queries. To process such queries, a straightforward approach is to simply glue the techniques developed for ranking-only and clustering-only together. This approach is costly since both ranking and clustering are treated as blocking post-processing tasks upon Boolean query results by existing techniques. We propose a summary-based evaluation method that utilizes bitmap index to seamlessly integrate Boolean conditions, clustering, and ranking. Experimental study shows that our approach significantly outperforms the straightforward one and maintains high clustering quality.

show abstract

“…Before running EM, the GMM's parameters must be initialized. We initialized the mean μ of each component with weighted k-means (Kerdprasop et al, 2005). As no prior information on the target's volumes is given, the covariance matrices Σ are initialized with the identity matrix I.…”

Section: Targets Of Attention Modelingmentioning

confidence: 99%

Probabilistic Mapping of Human Visual Attention from Head Pose Estimation

et al. 2017

View full text Add to dashboard Cite

Effective interaction between a human and a robot requires the bidirectional perception and interpretation of actions and behavior. While actions can be identified as a directly observable activity, this might not be sufficient to deduce actions in a scene. For example, orienting our face toward a book might suggest the action toward "reading." For a human observer, this deduction requires the direction of gaze, the object identified as a book and the intersection between gaze and book. With this in mind, we aim to estimate and map human visual attention as directed to a scene, and assess how this relates to the detection of objects and their related actions. In particular, we consider human head pose as measurement to infer the attention of a human engaged in a task and study which prior knowledge should be included in such a detection system. In a user study, we show the successful detection of attention to objects in a typical office task scenario (i.e., reading, working with a computer, studying an object). Our system requires a single external RGB camera for head pose measurements and a pre-recorded 3D point cloud of the environment.

show abstract

Weighted K-Means for Density-Biased Clustering

Cited by 49 publications

References 16 publications

Image matching with distinctive visual vocabulary

Image matching with distinctive visual vocabulary

Supporting ranking and clustering as generalized order-by and group-by

Probabilistic Mapping of Human Visual Attention from Head Pose Estimation

Contact Info

Product

Resources

About