A Measure for Objective Evaluation of Image Segmentation Algorithms

Unnikrishnan, R.; Pantofaru, Caroline; Hebert, Martial

doi:10.1109/cvpr.2005.390

Cited by 158 publications

(101 citation statements)

References 12 publications

(29 reference statements)

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“…While there exist many general purpose segmentation engines, studies have shown that none are particularly good at segmenting out individual objects [17,5]. Instead, we will follow [6,11] in using multiple different segmentations of the same image, in a way sampling the space of segmentations.…”

Section: Does Spatial Support Matter?mentioning

confidence: 99%

Improving Spatial Support for Objects via Multiple Segmentations

Malisiewicz

Efros²

2007

Procedings of the British Machine Vision Conference 2007

251

183

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Sliding window scanning is the dominant paradigm in object recognition research today. But while much success has been reported in detecting several rectangular-shaped object classes (i.e. faces, cars, pedestrians), results have been much less impressive for more general types of objects. Several researchers have advocated the use of image segmentation as a way to get a better spatial support for objects. In this paper, our aim is to address this issue by studying the following two questions: 1) how important is good spatial support for recognition? 2) can segmentation provide better spatial support for objects? To answer the first, we compare recognition performance using ground-truth segmentation vs. bounding boxes. To answer the second, we use the multiple segmentation approach to evaluate how close can real segments approach the ground-truth for real objects, and at what cost. Our results demonstrate the importance of finding the right spatial support for objects, and the feasibility of doing so without excessive computational burden.

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Section: Does Spatial Support Matter?mentioning

confidence: 99%

Improving Spatial Support for Objects via Multiple Segmentations

Malisiewicz

Efros²

2007

Procedings of the British Machine Vision Conference 2007

251

183

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“…These measures can be divided [23] into several classes: regionbased quality evaluation measures (taking into account the characteristics of the segmented regions), edge-based quality evaluation measures (taking into account the characteristics of boundaries of the segmented regions), measures based on information theory, and nonparametric measures. The first class includes the socalled directional Hamming distance [20], which is asymmetrical measure, and normalized Hamming distance [20], Local / global consistency errors [23], etc. The second class includes the precision and recall measures [21], earth movers distance [22], and others.…”

Section: Segmentation Quality Evaluationmentioning

confidence: 99%

“…This complicates the clear choice to any particular measure of the segmentation quality. Given the fact that the study of measures of segmentation quality is not the main purpose of this work, in this paper we use the consistency errors [23] and Rand index [24] as one of the most commonly used measures.…”

Section: Segmentation Quality Evaluationmentioning

confidence: 99%

Hyperspectral image segmentation using dimensionality reduction and classical segmentation approaches

Myasnikov¹

2017

Computer Optics

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Unsupervised segmentation of hyperspectral satellite images is a challenging task due to the nature of such images. In this paper, we address this task using the following three-step procedure. First, we reduce the dimensionality of the hyperspectral images. Then, we apply one of classical segmentation algorithms (segmentation via clustering, region growing, or watershed transform). Finally, to overcome the problem of over-segmentation, we use a region merging procedure based on priority queues. To find the parameters of the algorithms and to compare the segmentation approaches, we use known measures of the segmentation quality (global consistency error and rand index) and well-known hyperspectral images.Keywords: hyperspectral image, segmentation, clustering, watershed transform, region growing, region merging, segmentation quality measure, global consistency error, rand index.Citation: Myasnikov EV. Hyperspectral image segmentation using dimensionality reduction and classical segmentation approaches. Computer Optics 2017; 41(4): 564-572. DOI: 10.18287/2412DOI: 10.18287/ -6179-2017.Acknowledgments: The reported study was funded by the Russian Foundation for Basic Research (RFBR) grants 16-29-09494 ofi_m and 16-37-00202 mol_a. IntroductionA hyperspectral image is a three-dimensional array having two spatial dimensions, and one spectral dimension. Every pixel of a hyperspectral image is a vector containing hundreds of components corresponding to a wide range of wavelengths. Compared to grayscale and multispectral images, hyperspectral images offer new opportunities allowing to extract information about materials (components) located on images. Thanks to these unique properties, hyperspectral images are used in agriculture, medicine, chemistry and many other fields.However, high dimensionality of hyperspectral images often makes it impossible to directly apply traditional image analysis techniques to such images. For this reason, hyperspectral image analysis became an extensively studying area last years. In this paper, we consider the segmentation of hyperspectral images, which is one of the most important tasks in hyperspectral image analysis [1 -6]. Other important tasks include, for example, classification [7], detection of anomalies [8], etc.Image segmentation is the process of partitioning an image into connected regions with homogenous properties. In image analysis, segmentation methods are usually divided into three classes [1]: feature-based methods, region-based methods, and edge-based methods.Feature-based methods split all image pixels into subsets, based on their values or derived properties. Thus, first class of methods operates in spectral or derived space. This class includes methods based on clustering [2,3]. Region-based and edge-based methods operate on a spatial domain. Region-based methods use some homogeneity criterion to detect regions in an image. This class includes methods based on region growing, and watershed transformation [4,5]. Edge-based methods use the properties of discont...

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“…It is important to evaluate the quality of a segmented image obtained by various clustering algorithm because the results of various clustering algorithms gives different results. The NPR index [23] is the generalized version of rand index, which is used to measure the quality of clustering results. The NPR uses the hand-labeled set of ground-truth segmentation to perform a comparison between two image segmentation algorithms.…”

Section: A Validation Measuresmentioning

confidence: 99%

Color Image Segmentation via Improved K-Means Algorithm

Kumar¹,

Kumar²

2016

ijacsa

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Abstract-Data clustering techniques are often used to segment the real world images. Unsupervised image segmentation algorithms that are based on the clustering suffer from random initialization. There is a need for efficient and effective image segmentation algorithm, which can be used in the computer vision, object recognition, image recognition, or compression. To address these problems, the authors present a density-based initialization scheme to segment the color images. In the kernel density based clustering technique, the data sample is mapped to a high-dimensional space for the effective data classification. The Gaussian kernel is used for the density estimation and for the mapping of sample image into a highdimensional color space. The proposed initialization scheme for the k-means clustering algorithm can homogenously segment an image into the regions of interest with the capability of avoiding the dead centre and the trapped centre by local minima phenomena. The performance of the experimental result indicates that the proposed approach is more effective, compared to the other existing clustering-based image segmentation algorithms. In the proposed approach, the Berkeley image database has been used for the comparison analysis with the recent clustering-based image segmentation algorithms like kmeans++, k-medoids and k-mode.

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A Measure for Objective Evaluation of Image Segmentation Algorithms

Abstract: Abstract

Cited by 158 publications

References 12 publications

Improving Spatial Support for Objects via Multiple Segmentations

Improving Spatial Support for Objects via Multiple Segmentations

Hyperspectral image segmentation using dimensionality reduction and classical segmentation approaches

Color Image Segmentation via Improved K-Means Algorithm

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