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The paper presents an evaluation of four clustering algorithms: k-means, average linkage, complete linkage, and Wardpsilas method, with the latter three being different hierarchical methods. The quality of the clusters created by the algorithms was measured in terms of cluster cohesiveness and semantic cohesiveness, and both quantitative and predicate-based similarity criteria were considered.Two...
Image clustering and categorization is a means for high-level description of image content. In the field of content-based image retrieval (CBIR), the analysis of gray scale images has got very much importance because of its immense application starting from satellite images to medical images. But the analysis of an image with such number of gray shades becomes very complex, so, for simplicity we cluster...
With the development of image retrieval system, the organizing and managing of image database effectively have been a key technology of users' retrieving. This paper firstly makes use of improved ant colony clustering algorithm in emotional clustering which based on image feature. This algorithm may identify who is the first ant by calculating the Euclidean distance, simulate ants' action of picking...
The paper presents a new approach for feature representation using semantic line groupings in an image. The algorithm uses the hypothesis in line with Gestalt laws of proximity that as a baseline in an image, semantic structures are formed by line segments placed in close proximity to each other. The algorithm uses line segments in an image to form semantic groups based on a minimum distance threshold...
Stop word detection is attempted in this work in the context of retrieval of document images in the compressed domain. Algorithms are presented to identify text lines and words and to cluster similar words to count word occurrence frequencies. A list of words with their occurrence frequencies is generated from a corpus of textual images. As stop words in any language show high occurrence frequencies,...
We generalize the notions of centroids and barycenters to the broad class of information-theoretic distortion measures called Bregman divergences. Because Bregman divergences are typically asymmetric, we consider both the left-sided and right-sided centroids and the symmetrized centroids, and prove that all three are unique. We give closed-form solutions for the sided centroids that are generalized...
In content-based image retrieval (CBIR), similarity measures vary according to the user, and it is difficult to build a retrieval system which reflects the user's similarity measures automatically. Regarding CBIR as consisting of feature extraction, coarse classification and detailed matching stages, this work aims at reflecting the user's similarity measures in coarse classification. After obtaining...
This paper presents a novel approach to automatically refining the original annotations of images. An existing image annotation method is used to obtain the candidate annotations for an image in advance. Then, low-level features are extracted automatically from all blocks in the image to construct a suitable multi-feature space. Next, the image is divided into nonoverlapping block-based structures...
In this paper, a K-means clustering (KMC) algorithm of automation determination the clustering number K is proposed, and a approach of region-based image segmentation is introduced based on our proposed algorithm. For this approach, firstly, a suitable color space is selected, the features of color, texture, and location are extracted, and the feature space is generated. Then, in this feature space,...
This paper proposes a novel classification method for image retrieval using gradient-based fuzzy c-means with divergence measure (GBFCM(DM)). GBFCM(DM) is a neural network-based algorithm that utilizes the Divergence Measure to exploit the statistical nature of the image data and thereby improve the classification accuracy. Experiments and results on various data sets demonstrate that the proposed...
We propose a content-based image retrieval prototype that combines the advantages of relevance feedback and image database categorization. Our approach is based on an algorithm that performs clustering and feature weighting simultaneously and can incorporate partial supervision information. This information, extracted from the userpsilas feedback through visual exploration and interaction, is used...
In this paper, we present a novel approach to contents-based image retrieval. The method hinges in the use of quasi-random sampling to retrieve those images in a database which are related to a query image provided by the user. Departing from random sampling theory, we make use of the EM algorithm so as to organize the images in the database into compact clusters that can then be used for stratified...
Although one of the most common usages of Internet is searching, especially in image search, the users are not satisfied due to many irrelevant results. In this paper we present a method to identify irrelevant results of image search on the Internet and re-rank the results so that the relevant results will have a higher priority within the list. The proposed method represents the similarity of images...
In a number of applications, especially those consisting of digital images, searching through large, unstructured databases based on sample sequence is often desirable. This paper evaluates symbolic-based approach for shape description of image elements and shapes. The idea is to identify each shape and to describe the shape as time series function. Symbolic-based algorithm is used to convert time...
In this work, we address the problem of image retrieval in the wavelet transform domain. More precisely, the wavelet coefficients are considered as realizations of a Generalized Gaussian Distribution (GGD) whose parameters are the signatures of the image. The retrieval procedure is based on a tree-structured search in the feature space that fully exploits the multiscale property of the image representation...
Clustering techniques can be adopted to analyze 3D model database and improve the retrieval performance. However, 3D model database lack valuable prior knowledge. Thus, it becomes difficult for the clustering methods to pre-decide the appropriate parameter's value. Moreover, clustering methods are short at handling outliers by treating outliers as "noise". The paper introduces a robust hierarchical...
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