The classical approach of using minimum cut criterion for clustering is often ineffective due to the existence of outliers in the data. This paper presents a novel normalized graph sampling algorithm for clustering that improves the solution of clustering via the incorporation of a priori constraint in a stochastic graph sampling procedure. The quality of the proposed algorithm is empirically e...

This paper presents the result of an adaptive region growing segmentation technique for color document images using an irregular pyramid structure. The emphasis is in the segmentation of textual components for subsequence extraction in document analysis. The segmentation is done in the RGB color space. A simple color distance measurement and a category of color thresholds are derived. The propo...

Flower image retrieval is a very important step for computer-aided plant species recognition. In this paper, we propose an efficient segmentation method based on color clustering and domain knowledge to extract flower regions from flower images. For flower retrieval, we use the color histogram of a flower region to characterize the color features of flower and two shape-based features sets, Cen...

This works deals with hybrid method for color image segmentation. The role of introducing the statistical features in clustering technique and that of data level and feature level fusion applied to color image segmentation are studied in this paper to obtain an optimal segmented image and to detect the fire in Forest images. The proposed segmentation approach is conceptually different and based...

and Normalized Cut Clustering Jui-Chen Wu, Jun-Wei Hsieh * , Yung-Sheng Chen, and Cheng-Min Tu Department of Electrical Engineering Yuan Ze University, Taiwan *[email protected] Abstract This paper proposes a novel approach for vehicle orientation detection using “vehicle color” and edge information based on clustering framework. To extract the “vehicle color”, this paper proposes a novel...

With the advancement in image capturing device, the image data been generated at high volume. If images are analyzed properly, they can reveal useful information to the human users. Content based image retrieval address the problem of retrieving images relevant to the user needs from image databases on the basis of low-level visual features that can be derived from the images. Grouping images i...

In this paper, new content-based image retrieval (CBIR) system based on color and shape feature combining with clustering concept that considers the similarities among images in database is proposed. Firstly, the color space of an image is quantized with 128 bin quantization and then color histogram is extracted from the quantized image. Secondly, the shape feature is extracted using horizontal...

In this paper, new content-based image retrieval (CBIR) system based on color and shape feature combining with clustering concept that considers the similarities among images in database is proposed. Firstly, the color space of an image is quantized with 128 bin quantization and then color histogram is extracted from the quantized image. Secondly, the shape feature is extracted using horizontal...

In this paper, new content-based image retrieval (CBIR) system based on color and shape feature combining with clustering concept that considers the similarities among images in database is proposed. Firstly, the color space of an image is quantized with 128 bin quantization and then color histogram is extracted from the quantized image. Secondly, the shape feature is extracted using horizontal...