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Cheap and highly-functional digital cameras are now readily available to the public. In contrast to traditional film-based cameras, tasks of refining, classification and clustering images is burdensome to camera users. Therefore, we need an automated procedure to assist manual management of digital photos. One way to overcome this problem is to provide a summarized view covering the entire set of...
This paper presents a method for unsupervised scene categorization. Our method aims at two objectives: (1) automatic feature selection for different scene categories. We represent images in a heterogeneous feature space to account for the large variabilities of different scene categories. Then, we use the information projection strategy to pursue features which are both informative and discriminative,...
Doppler imaging allows evaluation of blood flow patterns, direction, and velocity. The color (red, blue, and mosaic) signify the direction of the blood flow. By analyzing this color Doppler, it is possible to detect heart diseases like mitral and aortic stenosis, mitral, tricuspid, and aortic regurgitation, and Left Ventricle (LV) hypertrophy. We present 3 methods to extract low level features namely...
In this paper, a novel quantum fuzzy particle swarm optimization (QFPSO) approach has been proposed for image clustering. The particle swarm optimization is used to search the global optimal clustering center. Moreover, the quantum encoding is introduced and the quantum operation is implemented on each particle to overcome the premature convergence problem effectively. The experimental results showed...
Coloring of grayscale images finds its place in many areas such as medical imaging and coloring of black and white images and videos. However, manual coloring is time consuming and ill-posed problem. In this paper we present a semi-automatic framework for coloring of grayscale images. Given a grayscale image, the new framework operates by prompting the user to specify a source color image of similar...
In order to efficient, objective and comprehensive assessment of wheat flour processing accuracy, this paper introduces a new method to detect the wheat flour processing precision; it uses wheat flour three features of Whiteness, color, bran to design classifiers. The 240 different accuracy wheat sample images were analyzed and tested, experimental results show that CIE L*a*b*and OTSU algorithm can...
This paper presents an apple recognition method based on texture features and Maximum Expectation (EM) algorithm for Gaussian Mixture Model (GMM). The images were converted to HSV space from RGB space and the H channel images were selected as interested images to be processed. The images of H channel were divided into blocks of 8*8 pixels and the texture features of the blocks were calculated. Angular...
Concerning the problem of lane detection in the Lane Departure Warning (LDW) system, this paper presents one method to detect the region of lane marking based on the CIELab color features clustering. Color space can provide us more precious information than gray scale. This algorithm proves that it is feasible to recognize lane marking by using color clustering. According to the geometry feature of...
CBIR (Content-Based Image Retrieval) is a comprehensive technology which retrieves image on the basis of the content feature of images. Its main idea is to analyze the content feature of images in order to find the images with similar content in image database. CBIR has become a heated research area, for it combines image processing, pattern identification, computer vision, artificial intelligence...
Content-based image retrieval (CBIR), also known as query by image content (QBIC) and content-based visual information retrieval (CBVIR) is the application of computer vision to the image retrieval problem, that is, the problem of searching for digital images in large databases. It is increasingly evident that an image retrieval system has to be domain specific. In this paper, we present an algorithm...
The `fuzzy co-clustering algorithm for images (FCCI)' technique has been successfully applied to colour segmentation of medical images. The goal of this work is to extend this technique by the inclusion of texture features as a clustering parameter for detecting blotches in skin lesions based on colour information. The objective function is optimized using the bacterial foraging algorithm which gives...
A new photo retrieval system for mobile devices is proposed in this paper. The system can be used to search for photos with similar spatial layout effectively and efficiently, and it adopts a new algorithm that extracts features of image regions based on hardware K-Means clustering. Since K-Means is computationally intensive for real-time applications in embedded systems, it is necessary to accelerate...
Designing an OCR system for Indian languages in general is more complex than those of European languages due the linguistic complexity. Efforts are on the way for the development of efficient OCR systems for Indian languages, especially for Telugu, a popular South Indian language. In this paper, we proposed a method for reliable extraction of text line, word and character from document images of Telugu...
A new approach of fast color character extraction was proposed. Clustering algorithm was adopted in our method to differentiate between objective character regions and background regions on the premise that character regions are nearly monochromatic. However, the key point of this approach was how to select suitable elements' features based upon the original image information and character's information;...
Content-based image retrieval can be dramatically improved by providing a good initial clustering of visual data. The problem of image clustering is that most current algorithms are not able to identify individual clusters that exist in different feature subspaces. In this paper, we propose a novel approach for subspace clustering based on Ant Colony Optimisation and its learning mechanism. The proposed...
A methodology for clustering multi-relational data is proposed. Initially, tuple linkages in the database schema of the multi-relational entities are leveraged to virtually organize the available relational data into as many transactions, i.e. sets of feature-value pairs. The identified transactions are then partitioned into homogeneous groups. Each discovered cluster is equipped with a representative,...
To overcome the disadvantage of describing contents of an image only with global features, a new color cluster image retrieval algorithm based on object regions is proposed. In this paper, firstly, object regions are extracted from an image, and then a new color cluster algorithm is presented. The algorithm is developed based on HVS color space, histogram intersection and combination of two similarity...
Methods developed for image annotation usually make use of region clustering algorithms. Visual codebooks are generated from the region clusters of low level features. These codebooks are then, matched with the words of the text document related to the image, in various ways. In this paper, we supervise the clustering process by using three types of side information. The first one is the topic probability...
This paper describes an effective framework to perform image segmentation and find regions of interest (ROI) in a user input object in an interactive way. Similar image objects are then retrieved from a repository. The repository stores off-line trained feature data of image objects, which was obtained by applying feature extraction and dimension reduction analysis to the ROI. The advantage of our...
This paper proposes a fast and robust algorithm for traffic sign detection and recognition. The algorithm includes two stages: traffic sign detection and recognition. In the first stage, Adaboost algorithm based red pixels model of speed limit sign in the Lab color space is built. Then the model is used to extract area of latent speed limit signs. After that, the improved Hough Transform is used to...
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