Color- and texture-based image segmentation using EM and its application to content-based image retrieval

Belongie, Serge; Carson, Chad; Greenspan, Hayit; Malik, Jitendra

doi:10.1109/iccv.1998.710790

Cited by 352 publications

(200 citation statements)

References 21 publications

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“…Following the method in [5], each coherent region is modeled as a multivariate Gaussian. After learning the parameters sets (that is, the mean vector μ i and the covariance matrix Σ i .)…”

Section: Resultsmentioning

confidence: 99%

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Spatial Weighting for Bag-of-Visual-Words and Its Application in Content-Based Image Retrieval

Chen

Shen

2009

Advances in Knowledge Discovery and Data Mining

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Abstract. It is a challenging and important task to retrieve images from a large and highly varied image data set based on their visual contents. Problems like how to fill the semantic gap between image features and the user have attracted a lot of attention from the research community. Recently, the 'bag of visual words' approach exhibits very good performance in content-based image retrieval (CBIR). However, since the 'bag of visual words' approach represents an image as an unordered collection of local descriptors which only use the intensity information, the resulting model provides little insight about the spatial constitution and color information of the image. In this paper, we develop a novel image representation method which uses Gaussian mixture model (GMM) to provide spatial weighting for visual words and apply this method to facilitate content based image retrieval. Our approach is a simple and more efficient compared with the order-less 'bag of visual words' approach. In our method, firstly, we extract visual tokens from the image data set and cluster them into a lexicon of visual words. Then, we represent the spatial constitution of an image as a mixture of n Gaussians in the feature space and decompose the image into n regions. The spatial weighting scheme is achieved by weighting visual words according to the probability of each visual word belonging to each of the n regions in the image. The cosine similarity between spatial weighted visual word vectors is used as distance measurement between regions, while the image-level distance is obtained by averaging the pair-wise distances between regions. We compare the performance of our method with the traditional 'bag of visual words' and 'blobworld' approaches under the same image retrieval scenario. Experimental results demonstrate that the our method is able to tell images apart in the semantic level and improve the performance of CBIR.

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Section: Resultsmentioning

confidence: 99%

“…The Gaussian mixture model (GMM) [12] has been used to model the feature space and the spatial distribution of images [5] . In this paper, based on GMM, we present a novel spatial weighting scheme for visual words as follows.…”

Section: Gaussian Mixture Model For Spatial Weightingmentioning

confidence: 99%

Spatial Weighting for Bag-of-Visual-Words and Its Application in Content-Based Image Retrieval

Chen

Shen

2009

Advances in Knowledge Discovery and Data Mining

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“…The JSEG method (Deng et al, 2001) consists of two independent steps: colour quantization and region growing spatial segmentation on multiscale thematic maps from the first step. The Blobworld scheme aims to transform images into a small set of regions which are coherent in colour and texture (Belongie et al, 1998). This is achieved by clustering pixels in a joint colour-texture-position eight-dimensional feature space using the EM algorithm.…”

Section: Region Growingmentioning

confidence: 99%

“…The basic approach of a region growing algorithm (Pal et al, 1993;Belongie et al, 1998;Deng et al, 2001Deng et al, , 2004Scarpa et al, 2006Scarpa et al, , 2007 is to start from a seed regions (mostly one or few pixels) that are assumed to be inside the object to be segmented. The neighbouring pixels to every seed region are evaluated to decide if they should be considered part of the object or not.…”

Section: Region Growingmentioning

confidence: 99%

Unsupervised texture segmentation

Haindl

1998

Advances in Pattern Recognition

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“…Even systems using segments and local features such as Blob world are still far away from identifying objects reliably. No system offers interpretation of images or even medium level concepts as they can easily be captured with text [3]- [4]. This loss of information from an image to a representation by features is called the semantic gap [5].In earlier stages, features were calculated in the spatial domain, and the statistical nature of texture was taken into account in the procedure, which was based on the assumption that the texture information in an image I was contained in the overall or "average" spatial relationship which the gray tones in the image have to one another [6].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Ultra Sound Kidney Image Features for Image Retrieval by Gray Level Co-Occurrence Matrices

Manikandan¹,

Rajamani²,

Murugan³

2013

LNSE

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Abstract-Medical imaging is though expensive because of capital costs, is easy to perform because of its noninvasive nature. In order to provide better analysis and diagnosis, various features have been extracted from any images. In this paper many important features of the ultra sound kidney images have been extracted and analysis has been made for the proposed image retrieval from the image database. Quantitative establishment of use the features for detection of abnormalities in Ultra sound kidney images have been made. Here, we made an analysis that the texture has been used to discriminate among the various types of tissue in image applications. Various important features namely contrast, homogeneity, correlation, energy, autocorrelation, variance, co-variance, inertia, promenance, shade, dissimilarity, inverse difference moment (IDM), maximum probability and entropy have been extracted and analyzed for the application in connection of a image database for the image retrieval process.Index Terms-Extraction, features, statistical test image retrieval, ultra sound kidney image.I.

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Color- and texture-based image segmentation using EM and its application to content-based image retrieval

Cited by 352 publications

References 21 publications

Spatial Weighting for Bag-of-Visual-Words and Its Application in Content-Based Image Retrieval

Spatial Weighting for Bag-of-Visual-Words and Its Application in Content-Based Image Retrieval

Unsupervised texture segmentation

Analysis of Ultra Sound Kidney Image Features for Image Retrieval by Gray Level Co-Occurrence Matrices

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