Content-based Video Retrieval

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Hybrid Information Retrieval Model For Web Images

The Bing Bang of the Internet in the early 90's increased dramatically the number of images being distributed and shared over the web. As a result, image information retrieval systems were developed to index and retrieve image files spread over the Internet. Most of these systems are keyword-based which search for images based on their textual metadata; and thus, they are imprecise as it is vague to describe an image with a human language. Besides, there exist the content-based image retrieval systems which search for images based on their visual information. However, content-based type systems are still immature and not that effective as they suffer from low retrieval recall/precision rate. This paper proposes a new hybrid image information retrieval model for indexing and retrieving web images published in HTML documents. The distinguishing mark of the proposed model is that it is based on both graphical content and textual metadata. The graphical content is denoted by color features and color histogram of the image; while textual metadata are denoted by the terms that surround the image in the HTML document, more particularly, the terms that appear in the tags p, h1, and h2, in addition to the terms that appear in the image's alt attribute, filename, and class-label. Moreover, this paper presents a new term weighting scheme called VTF-IDF short for Variable Term Frequency-Inverse Document Frequency which unlike traditional schemes, it exploits the HTML tag structure and assigns an extra bonus weight for terms that appear within certain particular HTML tags that are correlated to the semantics of the image. Experiments conducted to evaluate the proposed IR model showed a high retrieval precision rate that outpaced other current models.Comment: LACSC - Lebanese Association for Computational Sciences, http://www.lacsc.org/; International Journal of Computer Science & Emerging Technologies (IJCSET), Vol. 3, No. 1, February 201

Cluster Oriented Image Retrieval System with Context Based Color Feature Subspace Selection

This paper presents a cluster oriented image retrieval system with context recognition mechanism for selection subspaces of color features. Our idea to implement a context in the image retrieval system is how to recognize the most important features in the image search by connecting the user impression to the query. We apply a context recognition with Mathematical Model of Meaning (MMM) and then make a projection to the color features with a color impression metric. After a user gives a context, the MMM retrieves the highest correlated words to the context. These representative words are projected to the color impression metric to obtain the most significant colors for subspace feature selection. After applying subspace selection, the system then clusters the image database using Pillar-Kmeans algorithm. The centroids of clustering results are used for calculating the similarity measurements to the image query. We perform our proposed system for experimental purpose with the Ukiyo-e image datasets from Tokyo Metropolitan Library for representing the Japanese cultural image collections

Image Indexing and Retrieval

The amount of pictorial data has been growing enormously with the expansion of WWW. From the large number of images, it is very important for users to retrieve required images via an efficient and effective mechanism. To solve the image retrieval problem, many techniques have been devised addressing the requirement of different applications. Problem of the traditional methods of image indexing have led to the rise of interest in techniques for retrieving images on the basis of automatically derived features such as color, texture and shape… a technology generally referred as Content-Based Image Retrieval (CBIR). After decade of intensive research, CBIR technology is now beginning to move out of the laboratory into the marketplace. However, the technology still lacks maturity and is not yet being used in a significant scale

Advanced content-based semantic scene analysis and information retrieval: the SCHEMA project

The aim of the SCHEMA Network of Excellence is to bring together a critical mass of universities, research centers, industrial partners and end users, in order to design a reference system for content-based semantic scene analysis, interpretation and understanding. Relevant research areas include: content-based multimedia analysis and automatic annotation of semantic multimedia content, combined textual and multimedia information retrieval, semantic -web, MPEG-7 and MPEG-21 standards, user interfaces and human factors. In this paper, recent advances in content-based analysis, indexing and retrieval of digital media within the SCHEMA Network are presented. These advances will be integrated in the SCHEMA module-based, expandable reference system

Iowa 100% E News, February 2002, Vol.2, no.2

Newsletter for the Information Technology Departmen

Image mining: trends and developments

[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in very large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining

Strange bedfellows? Keyword and conceptual search unite to make sense of relevant ESI in electronic discovery

In the brief history of electronic discovery, the latter part of the twentieth century witnessed the demise of paper by a digital hero that emancipated the content of paper documents with OCR and TIFF. This technology added a third dimension to the realm of 2D paper document review and production that lead to a sea change in discovery methods. By many accounts what we have before us is a three-stage evolution from paper to digital to clustering in order to overcome the problems of volume and complexity of ESI. The intent of this position paper is to describe the development of the digital hero and methodology that is emancipating the content and context of ESI – conceptual search that spans file formats, languages and technique, and includes keyword search on a common, shared index

Image based Search Engine for Online Shopping

This paper presents a method based on principle of content based image retrieval for online shopping based on color, HSV aiming at efficient retrieval of images from the large database for online shopping specially for fashion shopping. Here, HSV modeling is used for creating our application with a huge image database, which compares image source with the destination components. In this paper, a technique is used for finding items by image search, which is convenient for buyers in order to allow them to see the products. The reason for using image search for items instead of text searches is that item searching by keywords or text has some issues such as errors in search items, expansion in search and inaccuracy in search results. This paper is an attempt to help users to choose the best options among many products and decide exactly what they want with the fast and easy search by image retrieval. This technology is providing a new search mode, searching by image, which will help buyers for finding the same or similar image retrieval in the database store. The image searching results have been made customers buy products quickly. This feature is implemented to identify and extract features of prominent object present in an image. Using different statistical measures, similarity measures are calculated and evaluated. Image retrieval based on color is a trivial task. Identifying objects of prominence in an image and retrieving image with similar features is a complex task. Finding prominent object in an image is difficult in a background image and is the challenging task in retrieving images. We calculated and change the region of interest in order to increase speed of operation as well as accuracy by masking the background content. The Implementation results proved that proposed method is effective in recalling the images of same pattern or texture