A three-level clustering algorithm for color texture segmentation

Sujaritha, M.; Annadurai, S.

doi:10.1145/1741906.1742052

Cited by 2 publications

(3 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Rarely simple SOM is implemented directly on image segmentation. Some researchers modified and expanded the typical SOM [11], while others combined SOM with other methods [6,7,10,[12][13][14][15]. Araújo and Costa [11] presented a new SOM with a variable topology for image segmentation.…”

Section: Clustering and Image Segmentationmentioning

confidence: 99%

“…Although the target image segmented is simple comparing with natural images and some priori information could be obtained, their ideas have been enlightening to natural image segmentation research. Besides directly applying to color components, other used derived features in SOM [12,14,15]. Reference [12] presented an unsupervised image segmentation framework CTex (based on color and texture), that is, based on the adaptive inclusion of color and texture in the process of data partition.…”

Section: Clustering and Image Segmentationmentioning

confidence: 99%

“…Reference [12] presented an unsupervised image segmentation framework CTex (based on color and texture), that is, based on the adaptive inclusion of color and texture in the process of data partition. In [14], a fully automatic three-level clustering algorithm for color-texture segmentation was presented. SOM is used to identify the number of components and initialize the Gaussian mixture model.…”

Section: Clustering and Image Segmentationmentioning

confidence: 99%

See 2 more Smart Citations

Self-Organizing Map-Based Color Image Segmentation with k-Means Clustering and Saliency Map

Chi

2011

ISRN Signal Processing

View full text Add to dashboard Cite

Natural image segmentation is an important topic in digital image processing, and it could be solved by clustering methods. We present in this paper an SOM-based k-means method (SOM-K) and a further saliency map-enhanced SOM-K method (SOM-KS). In SOM-K, pixel features of intensity and L∗u∗v∗ color space are trained with SOM and followed by a k-means method to cluster the prototype vectors, which are filtered with hits map. A variant of the proposed method, SOM-KS, adds a modified saliency map to improve the segmentation performance. Both SOM-K and SOM-KS segment the image with the guidance of an entropy evaluation index. Compared to SOM-K, SOM-KS makes a more precise segmentation in most cases by segmenting an image into a smaller number of regions. At the same time, the salient object of an image stands out, while other minor parts are restrained. The computational load of the proposed methods of SOM-K and SOM-KS are compared to J-image-based segmentation (JSEG) and k-means. Segmentation evaluations of SOM-K and SOM-KS with the entropy index are compared with JSEG and k-means. It is observed that SOM-K and SOM-KS, being an unsupervised method, can achieve better segmentation results with less computational load and no human intervention.

show abstract