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The use of multimedia data such as images, audio and video etc in daily life led to a huge amount of images in fairly large image databases. Nowadays, the image becomes a focal source of information because it hides a precious knowledge. In Image Mining, the aim is to discover this knowledge and gives the relevant information or patterns that are presented. This paper is dedicated to a review on image...
Technology brings images as a communication media for humans. Image communication used today in many fields such as education, media, healthcare and in other domains. Based on image retrieval user input selection one of the most powerful technique and has been an active research direction for the couple of years. Various features are used for image retrieval. Most of the retrieval technique used image...
In this paper, we present the visualization of image databases based on their primitive features. Our approach is to have a visual navigation tool for allowing the exploration and exploitation of large image archives. The tool is able to project the content of a given image database based on the primitive feature space and to provide interaction between the final user and the huge amount of data....
Local patterns have two problems: 1) the traditional local patterns methods only consider the frequency of each pattern, and does not consider the co-occurrence information between adjacent pixels pairs in the image; 2)the traditional methods limit on the gray texture analysis, ignoring the importance of color information. To address above problems, a novel method is proposed for color image retrieval...
Feature extraction simplifies the amount of information needed to describe the properties of an image accurately. This paper measures the performance of a CBIR system based on texture feature against combination of both color and texture feature. A Gray Level Co-occurrence Matrix is calculated for computing the texture feature of an image. Using these textual parameters similar images are extracted...
The numbers of digital images are increasing day by day and mining from large databases is becoming harder & harder. Indexing image data based on text is tiresome and error prone. If the indexing based on low-level feature of the image then it may reduce the workload and mining become faster. In this research paper we propose an indexing technique which indexes the digital images in the database...
Multimedia and image mining representing the image objects clearly and efficiently because image objects are hard to define. Thus, we have to break the image object into meaningful components such as color, texture, shape, etc. Querying the image objects after representing them to retrieve the discovered knowledge. In the age of Big Data where Velocity, Variety and Volume are the challenges, variety...
Existing image retrieval frameworks use the low-level visual features extracted from images to learn a set of semantic categories and tend to discard the conceptual properties of these features. In this paper, we propose to use the conceptual information of the low-level image features to establish a correspondence between the image features and a set of high-level semantic categories. In doing so,...
We have become able to get enough approvable images of a target object just by submitting its object-name to a conventional keyword-based Web image search engine. However, because the search results rarely include its uncommon images, we can often get only its common images and cannot easily get exhaustive knowledge about its appearance (look and feel). As next steps of image searches in the Web,...
In this paper, we propose an example-based method for automatic image annotation. The advantage of this method is that it can determine the image annotation using former annotation experiences, which overcomes the shortcomings of manual-annotation. Also, the method can be extended as semi-automatic annotation, which provides users with a simple and convenient interface for annotation.
As is known to all, there are many kinds of wood. If we distinguish the wood's characteristics by our eyesight, we can't distinguish the category and property of the wood correctly, and this way will cost enormous workload. In this paper, we propose the keyblock distribution based wood image retrieval algorithm, in order to make the wood image retrieval algorithm more precise and objective. Keyblocks...
Context plays a valuable role in any image understanding task confirmed by numerous studies which have shown the importance of contextual information in computer vision tasks, like object detection, scene classification and image retrieval. Studies of human perception on the tasks of scene classification and visual search have shown that human visual system makes extensive use of contextual information...
An up-to-date comparison of state-of-the-art low-level color and texture feature extraction methods, for the purpose of content-based image retrieval (CBIR) is presented in this paper. The CBIR problem is motivated by the need to search the exponentially increasing space of image and video databases efficiently and effectively. We implement and compare color and texture feature extraction algorithms...
Research on image retrieval technology based on color feature, for the color histogram with a rotation, translation invariance of the advantages and disadvantages of lack of space, a color histogram and color moment combination image retrieval. The theory is a separate color images and color histogram moment of extraction, and then two methods of extracting color feature vector weighted to achieve...
This paper presents an interface of image query system for content-based image retrieval.The proposed interface can split images into the regions and select an target region from the separated regions. We apply content-based image retrieval to the selected region as a query.The images in databases are also split into the areas in advance.The content features such as color and texture are extracted...
Content-based image retrieval (CBIR) considers the characteristics of the image itself, for example its shapes, colors and textures. The current approaches to CBIR differ in terms of which image features are extracted. Recent work deals with combination of distances or scores from different and independent representations. This work attempts to induce high level semantics from the low level descriptors...
Automatic image annotation is the key to semantic-based image retrieval. In this paper we formulate image annotation as a supervised multi-class labeling problem. The relationship between low-level visual features and semantic concepts is found by supervised Bayesian learning. Color and texture features form two separate vectors, for which two independent Gaussian mixture models (GMM) are estimated...
A method of detecting text regions in images which combines grayscale decomposition and stroke extraction is proposed. By checking the consistency of the two text features, text-like connected components are grouped together to generate text line regions in the processed image. It shows good performance on efficiently detecting image text rendered in relatively complex backgrounds.
In multi-camera surveillance systems, it is important to track the same person across multiple cameras. It is also desirable to recognize the individuals who have been previously observed in a single-camera system. The method that represents a object image using a bag of visual words has been commonly used in image retrieval applications. For recognizing people, it can outperform the methods mainly...
Most of the existing Content Based Image Retrieval algorithms are implemented in spatial domain. In order to save the time in images decompression, a novel image retrieval based on DCT dominant color and texture features in compressed domain is proposed. The mean value and variance of the DCT coefficients in DCT sub-blocks are used to describe image features. The mean values of those DCT sub-blocks...
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