Segmenting images using localized histograms and region merging

Beveridge, J. Ross; Griffith, J. S.; Kohler, Ralf; Hanson, Allen R.; Riseman, Edward M.

doi:10.1007/bf00158168

Cited by 136 publications

(55 citation statements)

References 35 publications

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“…Region-based methods: These methods gather similar pixels according to some homogeneity criteria [24], [25] and [26]. They are based on the assumption that pixels, which belong to the same homogeneous region, are more alike than pixels from different homogeneous regions.…”

Section: Boundary-based Methodsmentioning

confidence: 99%

Color image segmentation based on adaptive local thresholds

Navon

Miller

Averbuch

2005

Image and Vision Computing

143

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The goal of still color image segmentation is to divide the image into homogeneous regions.Object extraction, object recognition and object-based compression are typical applications that use still segmentation as a low-level image processing. In this paper we present a new method for color image segmentation. The proposed algorithm divides the image into homogeneous regions by local thresholds. The number of thresholds and their values are adaptively derived by an automatic process, where local information is taken into consideration. First, the watershed algorithm is applied. Its results are used as an initialization for the next step, which is iterative merging process. During the iterative process regions are merged and local thresholds are derived. The thresholds are determined one-by-one at different times during the merging process. Every threshold is calculated by local information on any region and its surroundings. Any statistical information on the input images is not given. The algorithm is found to be reliable and robust to different kind of images.

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Section: Boundary-based Methodsmentioning

confidence: 99%

Color image segmentation based on adaptive local thresholds

Navon

Miller

Averbuch

2005

Image and Vision Computing

143

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“…However, each functional measures distinct features and, although the second functional selects the best par-tition, the first one decides how the hierarchical structure is scanned through [10].…”

Section: The Proposed Algorithmmentioning

confidence: 99%

“…A hierarchical methodology is mostly designed as an optimisation problem to reach general sub-optimal values for an objective function that measures the "quality" of an image partition. Moreover, hierarchical approaches are commonly combined with a similarity criterion between regions that uses all the information extracted from the regions in order to decide if they should be merged or not in a region growing scheme [10,11]. Two main reasons lead us to choose a hierarchical strategy in our approach:…”

Section: Introductionmentioning

confidence: 99%

Unsupervised colour image segmentation by low-level perceptual grouping

Martínez-Usó

Pla

García-Sevilla

2011

Pattern Anal Applic

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This paper proposes a new unsupervised approach for colour image segmentation. A hierarchy of image partitions is created on the basis of a function that merges spatially connected regions according to primary perceptual criteria. Likewise, a global function that measures the goodness of each defined partition is used to choose the best low-level perceptual grouping in the hierarchy. Contributions also include a comparative study with five unsupervised colour image segmentation techniques. These techniques have been frequently used as a reference in other comparisons. The results obtained by each method have been systematically evaluated using four well-known unsupervised measures for judging the segmentation quality. Our methodology has globally shown the best performance, obtaining better results in three out of four of these segmentation quality measures. Experiments will also show that our proposal finds low-level perceptual solutions that are highly correlated with the ones provided by humans.

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“…To improve upon the unreliability of the isolated local features such as used in these methods, [5] uses simple homogeneity criteria to split and merge tiles defined by an a priori tessellation method to obtain more stable segmentations, and thus more meaningful regions, ttowever, a priori chosen, low level criteria do not yield meaningful candidate regions for canonical class descriptions from real images which is the objective of this paper. Methods have been developed that integrate a priori domain knowledge into the segmentation which improves segmentation at the expense of making the methods domain specific [19,16,18,12,3,6].…”

Section: Previous Workmentioning

confidence: 99%

Learning multiscale image models of 2D object classes

Perrin

Ahuja

Srinivasa

1997

Computer Vision — ACCV'98

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This paper is concerned with learning the canonical gray scale structure of the images of a class of objects. Structure is defined in terms of the geometry and layout of salient image regions that characterize the given views of the objects. The use of such structure based learning of object appearence is motivated by the relative stability of image structure over intensity values. A multiscale segmentation tree description is antomatically extracted for all sample images which are then matched to construct a single canonical representative which serves as the model 0fthe class. Different images are selected as prototypes, and each prototype tree is refined to best match the rest of the class. The model tree for the class is that tree which is best supported over all the initializations with different prototypes.Matching is formulated as a problem of finding the best mapping from regions of example images to those of the model tree, and implemented as a problem in incremental refinement of the model tree using a learning approach. Experiments are reported on a face image database. The results demonstrate that a reasonable model of facial geometry and topology is learnt which includes prominent facial features.

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Segmenting images using localized histograms and region merging

Cited by 136 publications

References 35 publications

Color image segmentation based on adaptive local thresholds

Color image segmentation based on adaptive local thresholds

Unsupervised colour image segmentation by low-level perceptual grouping

Learning multiscale image models of 2D object classes

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