Fast K-means algorithm based on a level histogram for image retrieval

Lin, Chuen-Horng; Chen, Chun‐Chieh; Lee, Hsin-Lun; Liao, Jan‐Ray

doi:10.1016/j.eswa.2013.11.017

Cited by 68 publications

(21 citation statements)

References 25 publications

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“…It can be described as four steps: (1) extract visual words from images by local feature descriptors like SURF [6], SIFT [7][8], PCA-SIFT [9], (2) construct the visual vocabulary by the clustering algorithms like k-means [10][11] and the random clustering forests [12], (3) quantify the images by the histogram of the extracted words in the vocabulary, and (4) use the sample images to train and test the classifier. The direct using of BOW model has been proved to be well applied to the classification of the repeated/near-duplicate images.…”

Section: Bag Of Words Modelmentioning

confidence: 99%

Objects Classification for Mobile Robots Using Hierarchic Selective Search Method

Cao

et al. 2016

MATEC Web Conf.

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Abstract. Aiming at determining the category of an image captured from mobile robots for intelligent application, classification with the bag-of-words model is proved effectively in near-duplicate/planar images. When it comes to images from mobile robots with complex background, does it still work well? In this paper, based on the merging criterion improvement, a method named hierarchical selective search is proposed hierarchically extracting complementary features to form a combined and environment-adaptable similarity measurement for segmentation resulting a small and high-quality regions set. Simultaneously those regions rather than a whole image are used for classification. As a result, it well improved the classification accuracy and make the bog-of-word model still work well on classification for mobile robots. The experiments on hierarchical selective search show its better performance than selective search on two task datasets for mobile robots. The experiments on classification shows the samples from regions are better than those original whole images. The advantage of less quantity and higher quality object regions from hierarchical selective search is more prominent when it comes to those special tasks for mobile robots with scarce data.

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Section: Bag Of Words Modelmentioning

confidence: 99%

Objects Classification for Mobile Robots Using Hierarchic Selective Search Method

Cao

et al. 2016

MATEC Web Conf.

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“…Specifically, the algorithm is well known for clustering of data such as images [7], video object segmentation [8], document clustering [9] etc. However, one of the drawbacks of the algorithm is its challenges of grouping categorical variables; k-means can only cluster numeric values.…”

Section: Related Workmentioning

confidence: 99%

Cluster Analysis of Data Points using Partitioning and Probabilistic Model-based Algorithms

Raheem¹,

Hauwau²,

O.³

2014

IJAIS

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Exploring the dataset features through the application of clustering algorithms is a viable means by which the conceptual description of such data can be revealed for better understanding, grouping and decision making. Some clustering algorithms, especially those that are partitionedbased, clusters any data presented to them even if similar features do not present. This study explores the performance accuracies of partitioning-based algorithms and probabilistic model-based algorithm. Experiments were conducted using kmeans, k-medoids and EM-algorithm. The study implements each algorithm using RapidMiner Software and the results generated was validated for correctness in accordance to the concept of external criteria method. The clusters formed revealed the capability and drawbacks of each algorithm on the data points.

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“…To reduce the time complexity, the histogram of the pixel values such as grayscale values and brightness values is commonly used. In digital image processing, for a gray-level image, each pixel has a discrete gray-level integer value, which is ranging from 0 to 255 (Lin et al, 2014). For this reason, a gray-level histogram has 256 bins such that each represents a gray scale value.…”

Section: Introductionmentioning

confidence: 99%

“…For the measurement of the closeness between the elements and the cluster centers in the clustering algorithm, the Euclidean distance has been used widely in the literature (Lin et al, 2014;Cheng et al, 2001;Rupali and Shweta, 2014). When the K-means algorithm is applied to a digital image for the segmentation purpose, the distances between many pixels and the cluster centers must be calculated in each iteration of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

Fast K-Means Color Image Clustering With Normalized Distance Values

Baykan

Sağlam

2018

Scitech

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ABSTRACT:Image segmentation is an intermediate image processing stage in which the pixels of the image are grouped into clusters such that the data resulted from this stage is more meaningful for the next stage. Many clustering methods are used widely to segment the images. For this purpose, most clustering methods use the features of the image pixels. While some clustering method consider the local features of images by taking into account the neighborhood system of the pixels, some consider the global features of images. The algorithm of the K-means clustering method, that is easy to understand and simple to put into practice, performs by considering the global features of the entire image. In this algorithm, the number of cluster is given by users initially as an input value. For the segmentation process, if the distribution of the pixels over a histogram is used, the algorithm runs faster. The values in the histogram must be discrete in a certain range. In this paper, we use the Euclidean distance between the color values of the pixels and the mean color values of the entire image for taking advantage of the every color values of the pixels. To obtain a histogram that consists of discrete values, we normalize the distance value in a specific range and round the values to the nearest integers for discretization. We tested the versions of K-means with the gray-level histogram and the distance value histogram on an urban image dataset getting from ISPRS WG III/4 2D Semantic Labeling dataset. Comparing the two histograms, the distance value histogram proposed in this paper is better than the gray-level histogram. Key Words: Clustering, Histogram, Image segmentation, K-meansNormalize Edilmiş Uzaklık Değerleri ile Hızlı K-ortalama Renkli Görüntü Segmentasyonu ÖZ: Görüntü segmentasyonu, görüntü piksellerinin kümelere gruplandığı orta seviye bir görüntü işleme aşamasıdır. Öyle ki, bu şamadan elde edilen veri daha sonraki aşamalar için önceki veriye göre daha anlamlı hale gelmiş olur. Birçok kümeleme metodu, görüntü segmentasyonu amacıyla yaygın bir şekilde kullanılmaktadır. Bu amaçla, çoğu kümeleme metodu görüntü piksellerinin özelliklerini kullanmaktadır. Bazı kümeleme metotları piksellerin komşuluk sistemini kullanarak görüntünün yerel özelliklerini ele alırken, bazıları da görüntünün genel özelliklerini ele almaktadır. Anlaşılması kolay ve uygulaması basit olan K-ortalama algoritması, bütün görüntünün özelliklerini ele alarak segmentasyon yapmaktadır. Bu algoritmada, küme sayısı başlangıç giriş değeri olarak kullanıcı tarafından verilmektedir. Bu segmentasyon işlemi için, eğer piksellerin bir histogram üzerindeki dağılımı kullanılırsa algoritma daha hızlı çalışmaktadır. Bu histogram üzerindeki değerler belirli bir aralıkta ve ayrık olmak zorundadır. Bu çalışmada, piksellerin her bir renk değerinden faydalanmak için piksellerin renk değerleri ile görüntünün ortalama renk değerleri arasındaki Öklit uzaklığı kullanılmıştır. Ayrık değerlerden oluşan bir histogram elde etmek için, uzaklık değerlerini belirli değer ara...

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Fast K-means algorithm based on a level histogram for image retrieval

Cited by 68 publications

References 25 publications

Objects Classification for Mobile Robots Using Hierarchic Selective Search Method

Objects Classification for Mobile Robots Using Hierarchic Selective Search Method

Cluster Analysis of Data Points using Partitioning and Probabilistic Model-based Algorithms

Fast K-Means Color Image Clustering With Normalized Distance Values

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