A Comprehensive Review on the Relevance Feedback in Visual Information Retrieval

Abstract-Visual information retrieval in images and video has been developing rapidly in our daily life and is an important research field in content-based information indexing and retrieval, automatic annotation and structuring of images. Visual information system can make the use of relevance feedback so that the user progressively refines the search result by marking images in the result as relevant , not relevant or neutral to the search query and then repeating the search with the new information. With a comprehensive review as the main portion, this paper also suggested some novel solutions and perspectives throughout the discussion. Introduce the concept of Negative bootstrap, opens up interesting avenues for future research. Keywords-Bootstrapping, CBIR (Content Based Image Retrieval), Relevance feedback VIR (Visual Information Retrieval). I. INTRODUCTION There has been a renewed spurt of research activity in Visual Information Retrieval. Basically two kinds of information are associated with a visual object (image or video): information about the object, called its metadata, and information contained within the object, called visual features. Metadata is alphanumeric and generally expressible as a schema of a relational or object-oriented database. Visual features are derived through computational processes typically image processing, computer vision, and computational geometric routines executed on the visual object. The simplest visual features that can be computed are based on pixel values of raw data, and several early image database systems [1] used pixels as the basis of their data models. In many specific applications, the process of visual feature extraction is limited by the availability of fast, implementable techniques in image processing and computer vision II. RELATED WORK Initially developed in document retrieval (Salton 1989), relevance feedback was transformed and introduced into content-based multimedia retrieval, mainly content-based image retrieval CBIR)[3]

Slovenian Virtual Gallery on the Internet

The Slovenian Virtual Gallery (SVG) is a World Wide Web based multimedia collection of pictures, text, clickable-maps and video clips presenting Slovenian fine art from the gothic period up to the present days. Part of SVG is a virtual gallery space where pictures hang on the walls while another part is devoted to current exhibitions of selected Slovenian art galleries. The first version of this application was developed in the first half of 1995. It was based on a file system for storing all the data and custom developed software for search, automatic generation of HTML documents, scaling of pictures and remote management of the system. Due to the fast development of Web related tools a new version of SVG was developed in 1997 based on object-oriented relational database server technology. Both implementations are presented and compared in this article with issues related to the transion between the two versions. At the end, we will also discuss some extensions to SVG. We will present the GUI (Graphical User Interface) developed specially for presentation of current exhibitions over the Web which is based on GlobalView panoramic navigation extension to developed Internet Video Server (IVS). And since SVG operates with a lot of image data, we will confront with the problem of Image Content Retrieval

Multi-layer Architecture For Storing Visual Data Based on WCF and Microsoft SQL Server Database

In this paper we present a novel architecture for storing visual data. Effective storing, browsing and searching collections of images is one of the most important challenges of computer science. The design of architecture for storing such data requires a set of tools and frameworks such as SQL database management systems and service-oriented frameworks. The proposed solution is based on a multi-layer architecture, which allows to replace any component without recompilation of other components. The approach contains five components, i.e. Model, Base Engine, Concrete Engine, CBIR service and Presentation. They were based on two well-known design patterns: Dependency Injection and Inverse of Control. For experimental purposes we implemented the SURF local interest point detector as a feature extractor and $K$-means clustering as indexer. The presented architecture is intended for content-based retrieval systems simulation purposes as well as for real-world CBIR tasks.Comment: Accepted for the 14th International Conference on Artificial Intelligence and Soft Computing, ICAISC, June 14-18, 2015, Zakopane, Polan

The Mirror MMDBMS architecture

Handling large collections of digitized multimedia data, usually referred to as multimedia digital libraries, is a major challenge for information technology. The Mirror DBMS is a research database system that is developed to better understand the kind of data management that is required in the context of multimedia digital libraries (see also URL http://www.cs.utwente.nl/~arjen/mmdb.html). Its main features are an integrated approach to both content management and (traditional) structured data management, and the implementation of an extensible object-oriented logical data model on a binary relational physical data model. The focus of this work is aimed at design for scalability

An MPEG-7 scheme for semantic content modelling and filtering of digital video

Abstract Part 5 of the MPEG-7 standard specifies Multimedia Description Schemes (MDS); that is, the format multimedia content models should conform to in order to ensure interoperability across multiple platforms and applications. However, the standard does not specify how the content or the associated model may be filtered. This paper proposes an MPEG-7 scheme which can be deployed for digital video content modelling and filtering. The proposed scheme, COSMOS-7, produces rich and multi-faceted semantic content models and supports a content-based filtering approach that only analyses content relating directly to the preferred content requirements of the user. We present details of the scheme, front-end systems used for content modelling and filtering and experiences with a number of users

An adaptive approach for image organisation and retrieval

We propose and evaluate an adaptive approach towards content-based image retrieval (CBIR), which is based on the Ostensive Model of developing information needs. We use ostensive relevance to capture the user's current interest and tailor the retrieval accordingly. Our approach supports content-assisted browsing, by incorporating an adaptive query learning scheme based on implicit feedback from the user. Textual and colour features are employed to characterise images. Evidence from these features are combined using the Dempster-Shafer theory of evidence combination. Results from a user-centred, task-oriented evaluation show that the ostensive interface is preferred over a traditional interface with manual query facilities. Its strengths are considered to lie in its ability to adapt to the user's need, and its very intuitive and fluid way of operation