Pixel clustering for color image segmentation

Kharinov, Mikhail V.

doi:10.1134/s0361768815050047

Cited by 14 publications

(6 citation statements)

References 11 publications

(16 reference statements)

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“…The lower gray convex curve still corresponds to optimal approximations. The dotted black curve describes the generation of the initial image approximation of

clusters in one or another high-speed agglomerative segmentation algorithm [ 9 , 10 ]. The bold arrow shows the decrease in the image approximation error as a result of processing the input approximation with

clusters by the CI method.…”

Section: CI Methods For Improving Structured Approximationsmentioning

confidence: 99%

“…In this case, the assumption that

is negligibly small compared to

and

turns out to be incorrect. In order to avoid calculating false minima

, in most cases it makes sense to replace the K-means method with a stronger K-meanless method, in which the reclassification of pixel sets is performed either by comparing the values of the objective functional

, as in [ 14 ], or by the equivalent Formula (8) for

, as in [ 13 ].…”

Section: K-meanless Methods For Improving Structured Approximationsmentioning

confidence: 99%

“…The black solid curve describes the superpixel hierarchy. The lower gray curve describes the sequence of optimal image approximations, and the red curve describes the object binary hierarchy of approximations obtained by the hierarchical Otsu method [ 9 , 10 , 26 ].…”

Section: Figurementioning

confidence: 99%

“…The aim of the paper is to describe a formal model for detecting objects in an image by modernized versions of classical cluster analysis methods [ 5 , 6 ] (Otsu’s methods [ 7 , 8 , 9 , 10 ], Ward’s [ 1 , 11 , 12 , 13 ], K-means [ 1 , 14 , 15 , 16 , 17 , 18 , 19 ] and combined splitting/merging clustering methods [ 11 , 20 , 21 , 22 ]), which reduce the image approximation error of approaching an image by its piecewise constant approximations. In this case, the requirement of minimizing the approximation error is to be fulfilled, which, as a rule, conflicts with the heuristic consideration of a priori information about objects.…”

Section: Introductionmentioning

confidence: 99%

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A Model of Pixel and Superpixel Clustering for Object Detection

2022

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The paper presents a model of structured objects in a grayscale or color image, described by means of optimal piecewise constant image approximations, which are characterized by the minimum possible approximation errors for a given number of pixel clusters, where the approximation error means the total squared error. An ambiguous image is described as a non-hierarchical structure but is represented as an ordered superposition of object hierarchies, each containing at least one optimal approximation in g0 = 1,2,..., etc., colors. For the selected hierarchy of pixel clusters, the objects-of-interest are detected as the pixel clusters of optimal approximations, or as their parts, or unions. The paper develops the known idea in cluster analysis of the joint application of Ward’s and K-means methods. At the same time, it is proposed to modernize each of these methods and supplement them with a third method of splitting/merging pixel clusters. This is useful for cluster analysis of big data described by a convex dependence of the optimal approximation error on the cluster number and also for adjustable object detection in digital image processing, using the optimal hierarchical pixel clustering, which is treated as an alternative to the modern informally defined “semantic” segmentation.

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“…The lower gray convex curve still corresponds to optimal approximations. The dotted black curve describes the generation of the initial image approximation of

clusters by the CI method.…”

Section: CI Methods For Improving Structured Approximationsmentioning

confidence: 99%

“…In this case, the assumption that

is negligibly small compared to

and

turns out to be incorrect. In order to avoid calculating false minima

, as in [ 14 ], or by the equivalent Formula (8) for

, as in [ 13 ].…”

Section: K-meanless Methods For Improving Structured Approximationsmentioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Model of Pixel and Superpixel Clustering for Object Detection

2022

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“…A pronounced improvement in object detection when images are merged into single one was noticed for stereopairs [1] and is used to recognize remote images of the same scene [2,3]. To develop an utilization of the idea, let's attach the input image to the image of object-of-interest examples and approach the joint image by a sequence of piecewise constant approximations in the color numbers from 1 to N , where N is the number of pixels in the image.…”

Section: Introductionmentioning

confidence: 99%

Example-Based Object Detection in the Attached Image

Kharinov

2022

Proceedings of the 32nd International Conference on Computer Graphics and Vision

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The paper solves the problem of detecting exemplified objects in a color image. A solution provides the representation of similar objects in the same colors, and different objects in different colors. This is achieved by combining images of object examples and a target image into a single joint image, which is represented in sequential number 1, 2, ..., etc. colors. The mentioned effect is demonstrated by detecting irises and pupils in a test image. It is explained by the fact that: a) the joint image is approximated by a hierarchy of approximations in sequential color numbers; b) the hierarchy of approximations is described by a convex sequence of approximation errors (values of the total squared error ); c) due to the convexity, the approximation errors are reduced for all approximations of the joint image. In the last explanation item, it is applied the operation of combining hierarchically organized objects into a single object, which is introduced in this paper. To produce the required hierarchy of image approximations Ward's pixel clustering is used. Ward's method is generalized for image processing by parts (within pixel subsets) that provides generation of multiple proper approximation hierarchies and accelerates the calculations. To do so, the so- called split-and-merge pixel cluster CI-method is embedded into Ward's generalized method to provide a real-life minimization of the error for image approximation in a fixed number of colors.

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