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With the development of image processing technology, how to efficiently retrieve the target image from mass images, becomes an urgent problem that needs to be solved. The existing low-level features of images, are lack of semantic information in traditional retrieval. To solve this problem, a method to extract color information by using twice clustering is put forward to improve the image retrieval...
Tongue diagnosis is one of the important topics in the field of Chinese traditional medicine (TCM), and color is the basic element of tongue image, it has important diagnostic value. This paper presents a novel approach to extract color feature of tongue images. First, we use iterative method to extract initial main color and initial number of main color, then we adopt GLA(Generalized Loyd Algorithm)...
Under the environment of big data, retrieval becomes a crucial technology and image retrieval is paid more attention and widely used. The paper proposes a second-order retrieval algorithm, of which can be used to retrieval the similar images. Firstly, extracting image sift features. Then, build frequency table of characteristic words by k-means clustering and bag of word algorithm. Finally, based...
In this paper we present a new method for content-based searching large image databases by comparing content of a query image and images stored in a database. The algorithm consists of three main steps: feature extraction, indexing and system learning. The feature extraction stage is based on two types of features (SURF keypoints and color). For indexing we use the k-means algorithm and for system...
The Content-based image retrieval (CBIR) systems and their application in different areas of development, are current research topics, however many of this systems are likely to fail due to use global features which cannot sufficiently capture the important properties of individual objects [1] because generally a typical query image includes relevant objects (e.g., Eiffel Tower), but also irrelevant...
This paper presents a study on the effectiveness of hierarchical clustering techniques application and classification for imaging context in the Content-Based Image Retrieval (CBIR). The study has the purpose to compare the obtained results from using different hierarchical clustering algorithms with various input parameters and configurations using two types of comparison techniques. The aims is...
Search and retrieval of images based on content has attracted considerable attention in recent years from the research community. Classification and Clustering algorithm are used to improve the result of Content based Image retrieval. This paper relies on a combination of color and edge features of image for the accurate retrieval of images. Color features are extracted by RGB color histogram and...
In content based image retrieval (CBIR) system, target images are sorted by feature similarities in terms of related query. Image classification is the important field in applications like security, biometrics, and in medical applications. An efficient image retrieval system is Hue, Saturation and Value (HSV) color space. This Classify the image into n number of areas based on different selected ranges...
Image searching is most interesting in the field of the computer vision. Every day many digital images are coming into the web. User is attracted to automatic image retrieval from this large dataset. Many methods which are introduced in this last ten years for image retrieval based on the similarity, size of database, image classification, similar group of images finding, performance of retrieval...
Image retrieval has been popular for several years. There are different system designs for content based image retrieval (CBIR) system. This paper propose a novel system architecture for CBIR system which combines techniques include content-based image and color analysis, as well as data mining techniques. To our best knowledge, this is the first time to propose segmentation and grid module, feature...
A new color-based image retrieval method is proposed in the paper. The quantization precision of this algorithm is higher than that of supervised method and its efficiency well than unsupervised way. First, through the distance-matrix of color, the sample image is clustered in a self-organizing way, thus its palette can be obtained. Based on the palette, other images in the database are mapped in...
In this paper, we propose a similarity-based image retrieval considering artifacts by self-organizing map with refractoriness. In the self-organizing map with refractoriness, the plural neurons in the Map Layer corresponding to the input can fire sequentially because of the refractoriness. The proposed image retrieval system considering artifacts using the self-organizing map with refractoriness makes...
Content Based Image Retrieval (CBIR) mainly contains two phases: first, to represent an image; second, to measure the dissimilarity between two images. Expectation-Maximization (EM) is a popular algorithm for clustering Gauss mixtures for the image representation, but the greedy nature of EM make it hard to get an optimal model for CBIR. In this paper, we introduce an improved EM algorithm for clustering...
In the era of data intensive management and discovery, the volume of images repositories requires effective means for mining and classifying digital image collections. Recent studies have evidenced great interest in image processing by “mining” visual information for objects recognition and retrieval. Particularly, image disambiguation based on the shape produces better results than traditional features...
Query by image content is a method to retrieve the most important images from the image database. It is an answer for the problem of searching for digital images in large database. A large number of relevance feedback schemes have been developed to improve the performance of content based image retrieval. In this paper we propose biased discriminant Euclidean embedding that form intraclass geometry...
This paper deals about retrieval images based on the content and image segmentation process such as very important step of image analysis and image processing. In present, the image processing and analyzing is still rapidly growing area of research. Image segmentation is the operation that divides image into set of different segments. We present a different type of image segmentation and impact quality...
The focus of this paper is to enhance retrieval performance and also to provide a better similarity distance computation. We develop a modified clustering algorithm for image retrieval where hierarchical algorithm is used to generate the initial number of clusters and the cluster centres. Experimental results show that the proposed method yields higher retrieval accuracy compared to the several conventional...
Content-based image retrieval relies on the use of efficient and effective image descriptors. One of the most important components of an image descriptor is concerned with the distance function used to measure how similar two images are. This paper presents a clustering approach based on distances correlation for computing the similarity among images. Conducted experiments involving shape, color,...
In this work a novel technique for representing the edges of an image is presented and the impact of this on image clustering is investigated. The characterization is performed in two steps: the “most important” edges are first selected by using both the Laplace operator and the Laguerre Gauss functions, and then the phase distribution of each edge point is estimated. The similarity is measured by...
Super-peer networks inherit the advantages of P2P networks, such as pooling together the shared data (images in our system) across peers, self-organizing, and fault-tolerance. In addition, they take advantage of the heterogeneity of capabilities across peers in load-balancing and network adaptation. A super-peer node operates as an equal peer, and as a server/parent to a set of peers. Content-based...
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