24,480 research outputs found
Color Textured Image Segmentation Using ICICM - Interval Type-2 Fuzzy C-Means Clustering Hybrid Approach
Segmentation is an essential process in image because of its wild application such as image analysis, medical image analysis, pattern reorganization, etc. Color and texture are most significant low-level features in an image. Normally, color-textured image segmentation consists of two steps: (i) extracting the feature and (ii) clustering the feature vector. This paper presents the hybrid approach for color texture segmentation using Haralick features extracted from the Integrated Color and Intensity Co-occurrence Matrix (ICICM). Then, Extended- Interval Type-2 Fuzzy C-means clustering algorithm is used to cluster the obtained feature vectors into several classes corresponding to the different regions of the textured image. Experimental results show that the proposed hybrid approach could obtain better cluster quality and segmentation results compared to state-of-art image segmentation algorithms
A New Method for Gray Level Image Thresholding Using Spatial Correlation Features and Ultrafuzzy Measure
One of the most recent techniques employed to estimate an optimal threshold of a gray level image for segmentation is ultrafuzzy measures. In this paper, we introduce relative fuzzy membership degree (RFMD) taking spatial correlation among the pixels in the image into account. We also propose a novel thresholding technique by combining two-dimensional histogram, which was determined by using the gray value of the pixels and the local average gray value of the pixels using ultrafuzziness and RFMD. Compared to fuzzy membership degree, RFMD of type-II fuzzy sets and ultrafuzzy measure is able to better segment critical gray level images. It was observed that the outcome is so encouraging in objective and subjective perspectives over the existing method for all varieties of images
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Fuzzy image segmentation using location and intensity information
The segmentation results of any clustering algorithm are very sensitive to the features used in the similarity measure and the object types, which reduce the generalization capability of the algorithm. The previously developed algorithm called image segmentation using fuzzy clustering incorporating spatial information (FCSI) merged the independently segmented results generated by fuzzy clustering-based on pixel intensity and pixel location. The main disadvantages of this algorithm are that a perceptually selected threshold does not consider any semantic information and also produces unpredictable segmentation results for objects (regions) covering the entire image. This paper directly addresses these issues by introducing a new algorithm called fuzzy image segmentation using location and intensity (FSLI) by modifying the original FCSI algorithm. It considers the topological feature namely, connectivity and the similarity based on pixel intensity and surface variation. Qualitative and quantitative results confirm the considerable improvements achieved using the FSLI algorithm compared with FCSI and the fuzzy c-means (FCM) algorithm for all three alternatives, namely clustering using only pixel intensity, pixel location and a combination of the two, for a range of sample of images
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Analysis of fuzzy clustering and a generic fuzzy rule-based image segmentation technique
Many fuzzy clustering based techniques when applied to image segmentation do not incorporate spatial relationships of the pixels, while fuzzy rule-based image segmentation techniques are generally application dependent. Also for most of these techniques, the structure of the membership functions is predefined and parameters have to either automatically or manually derived. This paper addresses some of these issues by introducing a new generic fuzzy rule based image segmentation (GFRIS) technique, which is both application independent and can incorporate the spatial relationships of the pixels as well. A qualitative comparison is presented between the segmentation results obtained using this method and the popular fuzzy c-means (FCM) and possibilistic c-means (PCM) algorithms using an empirical discrepancy method. The results demonstrate this approach exhibits significant improvements over these popular fuzzy clustering algorithms for a wide range of differing image types
Fuzzy Clustering for Image Segmentation Using Generic Shape Information
The performance of clustering algorithms for image segmentation are highly sensitive to the features used and types of objects in the image, which ultimately limits their generalization capability. This provides strong motivation to investigate integrating shape information into the clustering framework to improve the generality of these algorithms. Existing shape-based clustering techniques mainly focus on circular and elliptical clusters and so are unable to segment arbitrarily-shaped objects. To address this limitation, this paper presents a new shape-based algorithm called fuzzy clustering for image segmentation using generic shape information (FCGS), which exploits the B-spline representation of an object's shape in combination with the Gustafson-Kessel clustering algorithm. Qualitative and quantitative results for FCGS confirm its superior segmentation performance consistently compared to well-established shape-based clustering techniques, for a wide range of test images comprising various regular and arbitrary-shaped objects
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Fuzzy image segmentation of generic shaped clusters
The segmentation performance of any clustering algorithm is very sensitive to the features in an image, which ultimately restricts their generalisation capability. This limitation was the primary motivation in our investigation into using shape information to improve the generality of these algorithms. Fuzzy shape-based clustering techniques already consider ring and elliptical profiles in segmentation, though most real objects are neither ring nor elliptically shaped. This paper addresses this issue by introducing a new shape-based algorithm called fuzzy image segmentation of generic shaped clusters (FISG) that incorporates generic shape information into the framework of the fuzzy c-means (FCM) algorithm. Both qualitative and quantitative analyses confirm the superiority of FISG compared to other shape-based fuzzy clustering methods including, Gustafson-Kessel algorithm, ring-shaped, circular shell, c-ellipsoidal shells and elliptic ring-shaped clusters. The new algorithm has also been shown to be application independent so it can be applied in areas such as video object plane segmentation in MPEG-4 based coding
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Fuzzy Image Segmentation using Suppressed Fuzzy C-Means Clustering
Clustering algorithms are highly dependent on the features used and the type of the objects in a particular image. By considering object similar surface variations (SSV) as well as the arbitrariness of the fuzzy c-means (FCM) algorithm for pixellocation, a fuzzy image segmentation considering object surface similarity (FSOS) algorithm was developed, but it was unable to segment objects having SSV satisfactorily. To improve the effectiveness of FSOS in segmenting objects with SSV, thispaper introduces a new fuzzy image segmentation using suppressed fuzzy c-means clustering (FSSC) algorithm, which directly considers object SSV and incorporates the use of suppressed-FCM (SFCM) using pixel location. The algorithmalso perceptually selects the threshold within the range of human visual perception. Both qualitative and quantitative resultsconfirm the improved segmentation performance of FSSC compared with other algorithms including FSOS, FCM,possibilistic c-means (PCM) and SFCM for many different images
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Automatic Feature Set Selection for Merging Image Segmentation Results Using Fuzzy Clustering
The image segmentation performance of clustering algorithms is highly dependent on the features used and the type of objects contained in the image, which limits the generalization ability of such algorithms. As a consequence, a fuzzy image segmentation using suppressed fuzzy c-means clustering (FSSC) algorithm was proposed that merged the initially segmented regions produced by a fuzzy clustering algorithm, using two different feature sets each comprising two features from pixel location, pixel intensity and a combination of both, which considered objects with similar surface variations (SSV), the arbitrariness of fuzzy c-means (FCM) algorithm using pixel location and the connectedness property of objects. The feature set selection for the initial segmentation in the merging technique was however, inaccurate because it did not consider all possible feature set combinations and also manually defined the threshold used to identify objects having SSV. To overcome these limitations, a new automatic feature set selection for merging image segmentation results using fuzzy clustering (AFMSF) algorithm is proposed, which considers the best feature set selection and also calculates the threshold based upon human visual perception. Both qualitative and quantitative analysis prove the superiority of AFMSF algorithm compared with other clustering techniques including FSSC, FCM, possibilistic c-means (PCM) and SFCM, for different image types
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