Content-Based Image Retrieval (CBIR) has become one of the most active research areas in the past few years. Many visual feature representations have been explored and many systems built. While these research efforts establish the basis of CBIR, the usefulness of the proposed approaches is limited. Specifically, these efforts have relatively ignored two distinct characteristics of CBIR systems: (1) the gap between high level concepts and low level features; (2) subjectivity of human perception of visual content. This paper proposes a relevance feedback based interactive retrieval approach, which effectively takes into account the above two characteristics in CBIR. During the retrieval process, the user's high level query and perception subjectivity are captured by dynamically updated weights based on the user's feedback. The experimental results over more than 70,000 images show that the proposed approach greatly reduces the user's effort of composing a query and captures the user's i...

This paper provides a comprehensive survey of the technical achievements in the research area of image retrieval, especially content-based image retrieval, an area that has been so active and prosperous in the past few years. The survey includes 100+ papers covering the research aspects of image feature representation and extraction, multidimensional indexing, and system design, three of the fundamental bases of content-based image retrieval. Furthermore, based on the state-of-the-art technology available now and the demand from real-world applications, open research issues are identified and future promising research directions are suggested. C ○ 1999 Academic Press 1.

This paper provides a comprehensive survey of the technical achievements in the research area of Image Retrieval, especially Content-Based Image Retrieval, an area so active and prosperous in the past few years. The survey includes 100+ papers covering the research aspects of image feature representation and extraction, multi-dimensional indexing, and system design, three of the fundamental bases of Content-Based Image Retrieval. Furthermore, based on the state-of-the-art technology available now and the demand from real-world applications, open research issues are identified, and future promising research directions are suggested. 1. INTRODUCTION Recent years have seen a rapid increase of the size of digital image collections. Everyday, both military and civilian equipment generates giga-bytes of images. Huge amount of information is out there. However, we can not access to or make use of the information unless it is organized so as to allow efficient browsing, searching and retriev...

Content-Based Image Retrieval (CBIR) has become one of the most active research areas in the past few years. Many visual feature representations have been explored and many systems built. While these research efforts establish the basis of CBIR, the usefulness of the proposed approaches is limited. Specifically, these e#orts have relatively ignored two distinct characteristics of CBIR systems: (1) the gap between high level concepts and low level features; (2) subjectivity of human perception of visual content. This paper proposes a relevance feedback based interactive retrieval approach, which effectively takes into account the above two characteristics in CBIR. During the retrieval process, the user's high level query and perception subjectivity are captured by dynamically updated weights based on the user's relevance feedback. The experimental results show that the proposed approach greatly reduces the user's effort of composing a query and captures the user's information need more precise...

This paper provides an overview of current research in image information retrieval and provides an outline of areas for future research. The approach is broad and interdisciplinary and focuses on three aspects of image research (IR): text-based retrieval, content-based retrieval, and user interactions with image information retrieval systems. The review concludes with a call for image retrieval evaluation studies similar to TREC. Keywords: Information Science, Image Retrieval, CBIR, Introduction Interest in image retrieval has increased in large part due to the rapid growth of the World Wide Web. According to a recent study (Lawrence & Giles, 1999) there are 180 million images on the publicly indexable Web, a total amount of image data of about 3Tb [terabytes], and an astounding one million or more digital images are being produced every day (Jain, 93). The need to find a desired image from a collection is shared by many groups, including journalists, engineers, historians, designers...

: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : xii 1. INTRODUCTION : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 1 1.1 Digital Libraries : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 1 1.2 Visual Information Systems : : : : : : : : : : : : : : : : : : : : : : : 1 1.3 Image Databases : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 2 1.3.1 Characteristics of Image Data : : : : : : : : : : : : : : : : : : 5 1.3.2 Similarity Matches : : : : : : : : : : : : : : : : : : : : : : : : 5 1.3.3 Query Types : : : : : : : : : : : : : : : : : : : : : : : : : : : 5 1.3.4 Multiple Interpretations of an Image : : : : : : : : : : : : : : 7 1.3.5 Image Databases in a Distributed Environment : : : : : : : : 7 1.4 Motivation : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 8 1.4.1 Thesis Statement : : : : : : : : : : : : : : : : : : : : : : : : : 9 1.5 Thesis Overview : : : : : : : : : : : : : : : : : : : : : : : : : : : : : ...

Visual applications need to represent, manipulate, store, and retrieve both raw and processed visual data. Existing relational and object-oriented database systems fail to offer satisfactory visual data management support because they lack the kinds of representations, storage structures, indices, access methods, and query mechanisms needed for visual data. We argue that extensible visual object stores offer feasible and effective means to address the data management needs of visual applications. ISR4 is such a visual object store under development at the University of Massachusetts for the management of persistent visual information. ISR4 is designed to offer extensive storage and retrieval support for complex and large visual data, customizable buffering and clustering, and spatial and temporal indexing, along with a variety of multi-dimensional access methods and query languages.

We study the problem of representing images within a multimedia Database Management System (DBMS), in order to support fast retrieval operations without compromising storage efficiency. To achieve this goal, we propose new image coding techniques which combine a wavelet representation, embedded coding of the wavelet coefficients, and segmentation of image-domain regions in the wavelet domain. A bitstream is generated in which each image region is encoded independently of other regions, and without having to explicitly store information describing the regions. Simulation results show that our proposed algorithms achieve coding performance which compares favorably, both perceptually and objectively, to that achieved using state-of-the-art image/video coding techniques while additionally providing region-based support. 1 Introduction With advances in the computer technologies and the advent of the World-Wide Web, there has been an explosion in the amount and complexity of digital data bei...

With advances in the computer technologies and the advent of the World Wide Web, there has been an explosion in the amount and complexity of digital data being generated, stored, transmitted, analyzed, and accessed. Much of this information is multimedia in nature, including digital images, video, audio, graphics, and text data. In order to make use of this vast amount of data, efficient and effectivetechniques to analyze and retrievemultimedia information based on its content need to be developed.

Relevance feedback andsemantic retrieval have received extensive attention recently in the coinputer vision comnzunity. In this article, we present a semantic image query system with integrated feedback mechanism. Our system has two major components: the low-levelfeature space and the semantic space. In the low-level feature space, images are described by multidimensional vectors and are clustered based on the similarity of their contents. In the semantic space, the relationship among keywords is captured by a semantic hierarchy built by the aid of WordNet. Based on our system architecture, we propose a novel feedback solution for semaniic retrieval calledsemantic feedback, which allo~vs our system to interact with users directly at the semantic level. The short-term and long-term learning process of the semantic feedback substantially improves the image retrieval and annotation performance of our system. We demonstrate the effectiveness of our approach with experiments using 5,000 images from Core1 database. The signijicant contribution of this article is in the scenario of having a relatively small training data set compared to the testing data set. Some of the previous work in the same direction has chosen a very large training set and a veiy small testing set. Clearly, the problem that we try to solve is more realistic and challenging. Keywords: image analysis; image retrieval; multimedia database; natural languages