Ranjit Bhaskar

Automatic Image Segmentation by Dynamic Region Growth and Multiresolution Merging

Image segmentation is a fundamental task in many computer vision applications. In this paper, we propose a new unsupervised color image segmentation algorithm, which exploits the information obtained from detecting edges in color images in the CIE L*a*b* color space. To this effect, by using a color gradient detection technique, pixels without edges are clustered and labeled individually to identify some initial portion of the input image content. Elements that contain higher gradient densities are included by the dynamic generation of clusters as the algorithm progresses. Texture modeling is performed by color quantization and local entropy computation of the quantized image. The obtained texture and color information along with a region growth map consisting of all fully grown regions are used to perform a unique multiresolution merging procedure to blend regions with similar characteristics. Experimental results obtained in comparison to published segmentation techniques demonstrate the performance advantages of the proposed method.

Automatic color image segmentation by dynamic region growth and multimodal merging of color and texture information

Image segmentation is a fundamental task in many computer vision applications. In this paper, we present a novel unsupervised color image segmentation algorithm that utilizes color gradients, dynamic thresholding and texture modeling algorithms in a split and merge framework. To this effect, pixels without edges are clustered and labeled individually to identify the preliminary image content. Pixels that contain higher gradients are further classified by utilizing an iterative dynamic threshold generation technique and an appropriate entropy based texture model. The proposed algorithm was demonstrated successfully on an extensive database of images and benchmarked favorably against prior art.

Unsupervised image segmentation by automatic gradient thresholding for dynamic region growth in the CIE L*a*b* color space

In this paper, we propose a novel unsupervised color image segmentation algorithm named GSEG. This Gradient-based SEGmentation method is initialized by a vector gradient calculation in the CIE L*a*b* color space. The obtained gradient map is utilized for initially clustering low gradient content, as well as automatically generating thresholds for a computationally efficient dynamic region growth procedure, to segment regions of subsequent higher gradient densities in the image. The resultant segmentation is combined with an entropy-based texture model in a statistical merging procedure to obtain the final result. Qualitative and quantitative evaluation of our results on several hundred images, utilizing a recently proposed evaluation metric called the Normalized Probabilistic Rand index shows that the GSEG algorithm is robust to various image scenarios and performs favorably against published segmentation techniques.

An adaptive and progressive approach for efficient Gradient-based multiresolution color image segmentation

We propose an image segmentation methodology which exploits gradient information in a multiresolution framework. The proposed algorithm commences with a wavelet decomposition procedure to obtain a pyramidal representation of the input image, accompanied by an adaptive threshold generation scheme required for segregating regions of varying gradient densities. At low (coarse) resolution levels, progressive region growth, texture characterization, and region merging modules are integrated together to provide interim segmentations. These interim results are transferred from one resolution level to another as a-priori information, until the final result at the highest (original) resolution is achieved. Performance evaluation on several hundred images demonstrates that our algorithm computationally outperforms various published techniques, with superior segmentation quality.

Identification and ranking of relevant image content

In this paper, we present an image understanding algorithm for automatically identifying and ranking different image regions into several levels of importance. Given a color image, specialized maps for classifying image content namely: weighted similarity, weighted homogeneity, image contrast and memory colors are generated and combined to provide a metric for perceptual importance classification. Further analysis yields a region ranking map which sorts the image content into different levels of significance. The algorithm was tested on a large database of color images that consists of the Berkeley segmentation dataset as well as many other internal images. Experimental results show that our technique matches human manual ranking with 90% efficiency. Applications of the proposed algorithm include image rendering, classification, indexing and retrieval.