Split-and-merge Procedure for Image Segmentation using Bimodality Detection Approach

Image segmentation, the division of a multi-dimensional image into groups of associated pixels, is an essential step for many advanced imaging applications. Image segmentation can be performed by recursively splitting the whole image or by merging together a large number of minute regions until a specified condition is satisfied. The split-and-merge procedure of image segmentation takes an  intermediate level in an image description as the starting cutest, and thereby achieves a compromise between merging small primitive regions and recursively splitting the whole images to reach the desired final cutest. The proposed segmentation approach is a split-andmerge technique. The conventional split-and-merge algorithm is lacking in adaptability to the image semantics because of its stiff quadtree-based structure. In this paper, an automatic thresholding technique based on bimodality detection approach with non-homogeneity criterion is employed in the splitting phase of the split-and-merge segmentation scheme to directly reflect the image semantics to the image segmentation results. Since the proposed splitting technique depends upon homogeneity factor, some of the split regions may or may not split properly. There should be rechecking through merging technique between the two adjacent regions to overcome the drawback of the splitting technique. A sequential-arrange-based or a minimal spanning-tree based approach, that depends on data dimensionality of the weighted centroids of all split regions for finding the pair wise adjacent regions, is introduced. Finally, to overcome the problems caused by the splitting technique, a novel merging technique based on the density ratio of the adjacent pair regions is proposed. The algorithm has been tested on several synthetic as well as real life data and the results show the efficiency of the segmentation technique.Defence Science Journal, 2010, 60(3), pp.290-301, DOI:http://dx.doi.org/10.14429/dsj.60.35

Unsupervised segmentation of road images. A multicriteria approach

This paper presents a region-based segmentation algorithm which can be applied to various problems since it does not requir e a priori knowledge concerning the kind of processed images . This algorithm, based on a split and merge method, gives reliable results both on homogeneous grey level images and on textured images . First, images are divided into rectangular sectors . The splitting algorithm works independently on each sector, and uses a homogeneity criterion based only on grey levels . The mergin g is then achieved through assigning labels to each region obtained by the splitting step, using extracted feature measurements . We modeled exploited fields (data field and label field) by Markov Random Fields (MRF), the segmentation is then optimall y determined using the Iterated Conditional Modes (ICM) . Input data of the merging step are regions obtained by the splitting step and their corresponding features vector. The originality of this algorithm is that texture coefficients are directly computed from these regions . These regions will be elementary sites for the Markov relaxation process . Thus, a region- based segmentation algorith m using texture and grey level is obtained . Results from various images types are presented .Nous présentons ici un algorithme de segmentation en régions pouvant s'appliquer à des problèmes très variés car il ne tient compte d'aucune information a priori sur le type d'images traitées. Il donne de bons résultats aussi bien sur des images possédant des objets homogènes au sens des niveaux de gris que sur des images possédant des régions texturées. C'est un algorithme de type division-fusion. Lors d'une première étape, l'image est découpée en fenêtres, selon une grille. L'algorithme de division travaille alors indépendamment sur chaque fenêtre, et utilise un critère d'homogénéité basé uniquement sur les niveaux de gris. La texture de chacune des régions ainsi obtenues est alors calculée. A chaque région sera associé un vecteur de caractéristiques comprenant des paramètres de luminance, et des paramètres de texture. Les régions ainsi définies jouent alors le rôle de sites élémentaires pour le processus de fusion. Celui-ci est fondé sur la modélisation des champs exploités (champ d'observations et champ d'étiquettes) par des champs de Markov. Nous montrerons les résultats de segmentation obtenus sur divers types d'images

A hybrid methodology for data clustering

This thesis introduces and evaluates a new hybrid method for the searching for groups in data - a process referred to as cluster analysis. The Agglomerative - Partitional Clustering methodology (APC) proposed in this work is a novel solution to the clustering problem intended for use with large, noisy data sets and capable of recovering clusters of arbitrary shape. Large sample size, noise and nonhyperellipsoidal cluster shapes can create difficulties for many clustering algorithms. Many commonly used clustering techniques are too inefficient to handle large data sets found in many data analysis problems or are limited by the fact that they implicitly or explicitly define clusters as being hyperellipsoidal (i.e. “globular” in shape) and can therefore fail to recover other types of cluster structure. Moreover, the presence of noise can also make detection of cluster structures problematic, particularly for clustering techniques that are explicitly designed to handle nonhyperellipsoidal cluster structures. APC is able to circumvent these difficulties by hybridising a number of diverse approaches to clustering. Large data sets are dealt with by hybridising fast pattern partitioning techniques with hierarchical and density search methods. Arbitrary cluster shapes are handled by a unique linked line segment representation of cluster shape. In short, rather than representing clusters with their centroids, the clusters are represented via a piecewise linear approximation of the cluster structure. This enables APC to represent any cluster shape that is piecewise linearly approximatable. The purpose of this thesis, therefore, is to introduce APC and to evaluate the ability of APC to recover cluster structure under the conditions described above. First, it is argued that there is a dearth of clustering techniques that can process large, noisy data sets where there exists arbitrarily shaped clusters. Next, the APC approach to clustering is described in detail. Here it is discussed how APC is able to handle voluminous and noisy data without being constrained to any particular cluster shapes. Moreover, as APC represents a hybridisation of clustering strategies and techniques, different ways of implementing APC are also evaluated. The remainder of this thesis is concerned with the evaluation of APC. First, APC is empirically compared to other clustering methods via Monte Carlo simulation on a number of complex data sets. A wide variety of experimental conditions examining cluster shape, dispersion, noise and dimensionality are covered. The use of APC as a data reduction method is also examined. This final experiment also highlights the utility of the linked line segment representation of cluster shape proposed in this thesis. Finally, the concluding chapter summarises the results and limitations of this thesis and discusses some future directions this research could take