Improvement of the k-means clustering filtering algorithm

Lai, Jim Z. C.; Liaw, Yi‐Ching

doi:10.1016/j.patcog.2008.06.005

Cited by 35 publications

(21 citation statements)

References 11 publications

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“…A k-means clustering algorithm [52] is employed to calculate a set of cluster centres from a set of descriptors obtained from a series of baggage items. For each baggage item, a single BoW vector is then obtained via vector quantisation of the set of descriptors describing that bag [25].…”

Section: The Bag Of (Visual) Words (Bow) Modelmentioning

confidence: 99%

Object classification in 3D baggage security computed tomography imagery using visual codebooks

Flitton

Mouton

Breckon

2015

Pattern Recognition

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Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-pro t purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractWe investigate the performance of a Bag of (Visual) Words ( Optimal performance is achieved using the DH and DGH descriptors in conjunction with an uncertainty assignment methodology. Successful detection rates in excess of 97% for handguns and 89% for bottles and false-positive rates of approximately 2-3% are achieved. We demonstrate that the underlying imaging modality and the irrelevance of illumination and scale invariance within the transmission imagery context considered here, result in the favourable performance of simpler density histogram descriptors (DH, DGH) over 3D extensions of the well-established SIFT and RIFT feature descriptor approaches.

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Section: The Bag Of (Visual) Words (Bow) Modelmentioning

confidence: 99%

Object classification in 3D baggage security computed tomography imagery using visual codebooks

Flitton

Mouton

Breckon

2015

Pattern Recognition

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“…The convergence rate of this technique is fast and the computational load is less. A novel clustering algorithm called modified filtering algorithm (MFA) has been proposed in [14]. It is the improvement of the algorithm in [12].…”

Section: Literature Surveymentioning

confidence: 99%

HK-Means: A Heuristic Approach to Initialize and Estimate the Number of Clusters in Biological Data

2016

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K-means algorithm is one of the simplest and fastest clustering algorithms existing since more than four decades. One of the limitations of this algorithm is estimating number of clusters in advance. This algorithm also suffers from random initialization problem. This paper proposes a heuristic which initializes the cluster centers and estimates the number of clusters as a discrete value. The method estimates the number of clusters and initializes many cluster centers successfully for the clusters that are dense and separated significantly. The method selects a new cluster center in each iteration. The point selected is the point which is most dissimilar from the previously chosen points. The proposed algorithm is experimented on various synthetic data and the results are encouraging.

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“…⊳ On average, HCM is 92 times faster than FCM. This is because HCM uses hard memberships, which makes possible various computational optimizations that do not affect accuracy of the algorithm [51][52][53][54][55]. On the other hand, due to the intensive fuzzy membership calculations involved, accelerating FCM is significantly more difficult, which is why the majority of existing acceleration methods involve approximations [56][57][58][59][60].…”

Section: Comparison Of Hcm and Fcmmentioning

confidence: 99%

Hard versus fuzzy c-means clustering for color quantization

Wen

Çelebi

2011

EURASIP J. Adv. Signal Process.

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Color quantization is an important operation with many applications in graphics and image processing. Most quantization methods are essentially based on data clustering algorithms. Recent studies have demonstrated the effectiveness of hard c-means (k-means) clustering algorithm in this domain. Other studies reported similar findings pertaining to the fuzzy c-means algorithm. Interestingly, none of these studies directly compared the two types of c-means algorithms. In this study, we implement fast and exact variants of the hard and fuzzy c-means algorithms with several initialization schemes and then compare the resulting quantizers on a diverse set of images. The results demonstrate that fuzzy c-means is significantly slower than hard c-means, and that with respect to output quality, the former algorithm is neither objectively nor subjectively superior to the latter.

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Improvement of the k-means clustering filtering algorithm

Cited by 35 publications

References 11 publications

Object classification in 3D baggage security computed tomography imagery using visual codebooks

Object classification in 3D baggage security computed tomography imagery using visual codebooks

HK-Means: A Heuristic Approach to Initialize and Estimate the Number of Clusters in Biological Data

Hard versus fuzzy c-means clustering for color quantization

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