Distance measures for image segmentation evaluation

In this paper we present a study of evaluation measures that enable the quantification of the quality of an image segmentation result. Despite significant advances in image segmentation techniques, evaluation of these techniques thus far has been largely subjective. Typically, the effectiveness of a new algorithm is demonstrated only by the presentation of a few segmented images and is otherwise left to subjective evaluation by the reader. Such an evaluation criterion can be useful for different applications: the comparison of segmentation results, the automatic choice of the best fitted parameters of a segmentation method for a given image, or the definition of new segmentation methods by optimization. We first present the state of art of distance evaluation measures, and then, we compare several evaluation criteria

Performance evaluation of image segmentation

In spite of significant advances in image segmentation techniques, evaluation of these methods thus far has been largely subjective. Typically, the effectiveness of a new algorithm is demonstrated only by the presentation of a few segmented images that are evaluated by some method, or it is otherwise left to subjective evaluation by the reader. We propose a new approach for evaluation of segmentation that takes into account not only the accuracy of the boundary localization of the created segments but also the under-segmentation and over-segmentation effects, regardless to the number of regions in each partition. In addition, it takes into account the way humans perceive visual information. This new metric can be applied both to automatically provide a ranking among different segmentation algorithms and to find an optimal set of input parameters of a given algorithm

Classification of Jaw Bone Cysts and Necrosis via the Processing of Orthopantomograms

The authors analyze the design of a method for automatized evaluation of parameters in orthopantomographic images capturing pathological tissues developed in human jaw bones. The main problem affecting the applied medical diagnostic procedures consists in low repeatability of the performed evaluation. This condition is caused by two aspects, namely subjective approach of the involved medical specialists and the related exclusion of image processing instruments from the evaluation scheme. The paper contains a description of the utilized database containing images of cystic jaw bones; this description is further complemented with appropriate schematic repre¬sentation. Moreover, the authors present the results of fast automatized segmentation realized via the live-wire method and compare the obtained data with the results provided by other segmentation techniques. The shape parameters and the basic statistical quantities related to the distribution of intensities in the segmented areas are selected. The evaluation results are provided in the final section of the study; the authors correlate these values with the subjective assessment carried out by radiologists. Interestingly, the paper also comprises a discussion presenting the possibility of using selected parameters or their combinations to execute automatic classification of cysts and osteonecrosis. In this context, a comparison of various classifiers is performed, including the Decision Tree, Naive Bayes, Neural Network, k-NN, SVM, and LDA classifica¬tion tools. Within this comparison, the highest degree of accuracy (85% on the average) can be attributed to the Decision Tree, Naive Bayes, and Neural Network classifier

Image Segmentation using Euler Graphs

This paper presents a new algorithm for image segmentation problem using the concepts of Euler graphs in graph theory. By treating image as an undirected weighted non-planar finite graph (G), image segmentation is handled as graph partitioning problem. The proposed method locates region boundaries or clusters and runs in polynomial time. Subjective comparison and objective evaluation shows the efficacy of the proposed approach in different image domains

On Evaluating Video Object Segmentation Quality: A Perceptually Driven Objective Metric

The task of extracting objects in video sequences emerges in many applications such as object-based video coding (e.g., MPEG-4) and content-based video indexing and retrieval (e.g., MPEG-7). The MPEG-4 standard provides specifications for the coding of video objects, but does not address the problem of how to extract foreground objects in image sequences. Therefore, for specific applications, evaluating the quality of foreground/background segmentation results is necessary to allow for an appropriate selection of segmentation algorithms and for tuning their parameters for optimal performance. Many segmentation algorithms have been proposed along with a number of evaluation criteria. Nevertheless, formal psychophysical experiments evaluating the quality of different video foreground object segmentation results have not yet been conducted. In this paper, a generic framework for both subjective and objective segmentation quality evaluation is presented. An objective quality assessment method for segmentation evaluation is derived on the basis of perceptual factors through subjective experiments. The performance of the proposed method is shown on different state-of-the-art foreground/background segmentation algorithms and our method is compared to other objective methods which do not include perceptual factors. Moreover, on the basis of subjective results, weighting strategies are introduced into the proposed metric to meet the specificity of different segmentation applications e.g., video compression, video surveillance and mixed reality. Experimental results confirm the efficiency of the proposed approach

A Framework for Evaluating Video Object Segmentation Algorithms

Segmentation of moving objects in image sequences plays an important role in video processing and analysis. Evaluating the quality of segmentation results is necessary to allow the appropriate selection of segmentation algorithms and to tune their parameters for optimal performance. Many segmentation algorithms have been proposed along with a number of evaluation criteria. Nevertheless, no psychophysical experiments evaluating the quality of different video object segmentation results have been conducted. In this paper, a generic framework for segmentation quality evaluation is presented. A perceptually driven automatic method for segmentation evaluation is proposed and compared against an existing approach. Moreover, on the basis of subjective results, perceptual factors are introduced into the novel objective metric to meet the specificity of different segmentation applications such as video compression. Experimental results confirm the efficiency of the proposed evaluation criteria

On Evaluating Video Object Segmentation Quality: A Perceptually driven Objective Metric

Segmentation of moving objects in image sequences plays an important role in video processing and analysis. Evaluating the quality of segmentation results is necessary to allow the appropriate selection of segmentation algorithms and to tune their parameters for optimal performance. Many segmentation algorithms have been proposed along with a number of evaluation criteria. Nevertheless, no formal psychophysical experiments evaluating the quality of different video object segmentation results have been conducted. In this paper, a generic framework for segmentation quality evaluation is presented. A perceptually driven automatic method for segmentation evaluation is proposed and compared against state-of-the-art. Moreover, on the basis of subjective results, weighting strategies are introduced into the proposed objective metric to meet the specificity of different segmentation applications such as video compression and mixed reality. Experimental results confirm the efficiency of the proposed approach

Assessment of the Segmentation of RGB Remote Sensing Images: A Subjective Approach

The evaluation of remote sensing imagery segmentation results plays an important role in the further image analysis and decision-making. The search for the optimal segmentation method for a particular data set and the suitability of segmentation results for the use in satellite image classification are examples where the proper image segmentation quality assessment can affect the quality of the final result. There is no extensive research related to the assessment of the segmentation effectiveness of the images. The designed objective quality assessment metrics that can be used to assess the quality of the obtained segmentation results usually take into account the subjective features of the human visual system (HVS). A novel approach is used in the article to estimate the effectiveness of satellite image segmentation by relating and determining the correlation between subjective and objective segmentation quality metrics. Pearson’s and Spearman’s correlation was used for satellite images after applying a k-means++ clustering algorithm based on colour information. Simultaneously, the dataset of the satellite images with ground truth (GT) based on the “DeepGlobe Land Cover Classification Challenge” dataset was constructed for testing three classes of quality metrics for satellite image segmentation.This article belongs to the Special Issue The Quality of Remote Sensing Optical Images from Acquisition to UsersThis research has received funding from the Research Council of Lithuania (LMTLT), agreement No. S-MIP-19-27

How interaction methods affect image segmentation: User experience in the task

Interactive image segmentation is extensively used in photo editing when the aim is to separate a foreground object from its background so that it is available for various applications. The goal of the interaction is to get an accurate segmentation of the object with the minimal amount of human effort. To improve the usability and user experience using interactive image segmentation we present three interaction methods and study the effect of each using both objective and subjective metrics, such as, accuracy, amount of effort needed, cognitive load and preference of interaction method as voted by users. The novelty of this paper is twofold. First , the evaluation of interaction methods is carried out with objective metrics such as object and boundary accuracies in tandem with subjective metrics to cross check if they support each other. Second, we analyze Electroencephalography (EEG) data obtained from subjects perform- ing the segmentation as an indicator of brain activity. The experimental results potentially give valuable cues for the development of easy-to-use yet efficient interaction methods for image segmentation