Relevance Feedback in Content-Based Image Retrieval

Content-Based Image Retrieval (CBIR) systems are required to effectively harness information from ubiquitous image collections. Despite intense research efforts by the multidisciplinary CBIR community since early 1990s, apparently there is a mismatch between these advances and the one that is truly required to bring success to CBIR in the commercial market place. In this paper we provide an overview of approaches to CBIR. Major approaches to improving retrieval effectiveness via relevance feedback in text retrieval systems are discussed. How these relevance feedback techniques have been adopted to CBIR context and their effect on retrieval effectiveness are presented next. The need for test collections in advancing CBIR research is discussed. The paper concludes by pointing out open issues in CBIR and future research direction. 1 Content-Based Image Retrieval (CBIR) Digital images are produced at an ever increasing rate from diverse sources [27, 28, 70]. A contentbased image retrieval (CBIR) system is required to effectively harness information from these image repositories. Content-based retrieval is characterized by the ability of the system to retrieve relevan

Enterprise application integration using extensible Web services

This paper describes an approach to Enterprise Appli-cation Integration (EAI) using extensible Web services. The approach is demonstrated by building a real-world appli-cation for EAI in the financial services domain. Business drivers for and approaches to EAI are presented first. The manifestation of Web services in general and their role in EAI are discussed next. Financial services domain char-acteristics are presented. Business drivers that entail a strong need for functional extensibility in the financial ser-vices domain are described. Our proposed architecture for EAI which addresses functional extensibility is described. This architecture is based on the notion of extensible Web Services. We then present our implementation of the archi-tecture and practical challenges encountered in EAI. A brie

Modeling and Retrieving Images by Content

A content-based image retrieval system (CBIR) is required to effectively utilize information from image databases. Content-based retrieval is characterized by the ability of the system to retrieve relevant images based on their visual and semantic contents rather than by using atomic attributes or keywords assigned to them. In this paper, we provide a taxonomy for approaches to image retrieval and describe their characteristics and limitations. We examined a number of image database applications to discover their retrieval requirements and to structure the requirements from a domain independent perspective. This study enabled us to provide a taxonomy for image attributes and to propose a number of generic query operators. These operators are adequate to realize CBIR in a number of diverse applications. We propose a novel system architecture for CBIR that supports the generic query operators. The architecture is structured in a way to enable applications to inherit only those query oper..

Session S2D Enhancing Student Learning In Database Courses With Large Data Sets

Abstract- Rapidly increasing storage device capacities at ever decreasing costs have resulted in mushrooming of publicly available large data sets on the Web. In this paper, we describe a novel approach to teaching relational database course by using such data repositories. We demonstrate our approach using the Amazon.com product database, though the approach is generic and is applicable to other data repositories. The Amazon database is supposedly the largest product database ever in existence. We have used the Amazon Web Services API and.NET/C # application to extract a subset of the product database to enhance student learning in a relational database course. This realistic data served various activities of the course and provided a rich backdrop to demonstrate more interesting features of SQL and Oracle cost-based query optimization. Central to the course is a semester-long team project. We discuss the details of data extraction from Amazon.com, conceptual and logical data modeling, logical and physical database design, database creation and data loading, database querying, and database application development. Index Terms- Amazon e-commerce service, Enhancing student learning, Large data sets, Relational database course

A Unified Approach to Data Modeling for a Class of Image Database Applications

Recently, there has been widespread interest in various kinds of database management systems for managing information from images. Image Retrieval (IR) problem is concerned with retrieving images that are relevant to users' requests from a large collection of images, referred to as the image database. Since the application areas are very diverse, there seems to be no consensus as to what an image database system really is. Consequently, the features of the existing image database systems have essentially evolved from domain specific considerations. In response to this situation, we have introduced a unified framework for retrieval in image databases in [22]. Our approach to the image retrieval problem is based on the premise that it is possible to develop a data model and an associated retrieval model that can address the needs of a class of image retrieval applications. The key to domain independence is to separate the retrieval function from the image processing function. In this pap..

An Approach to Interactive Retrieval in Face Image Databases Based on Semantic Attributes

Image Retrieval (IR) problem is concerned with retrieving images that are relevant to users' requests from a large collection of images, referred to as the image database. A taxonomy for and the limitations of the existing approaches for image retrieval are discussed in [1]. Also, to alleviate some of the problems associated with these approaches, a unified framework for retrieval in image databases for a class of application areas is proposed in [1]. The framework provides a taxonomy for image attributes and identifies four generic types of retrieval bas... for RSA based on the repertory grid. The algorithm incorporates user relevance judgments as a means to deal with the inherent problems associated with the specification of semantic attributes. The algorithm is implemented and tested on the human face image database and the initial results are encouraging. In essence, we have developed an overall methodology/test bed to facilitate experimentation with different algorithms for RSA