Text-based Retrieval Research Articles

An effective World Wide Web image search engine

Effective World Wide Web image retrieval systems are required to locate relevant images as more and more images are used in HTML documents. There are two general approaches to image retrieval: one is based on text descriptions and the other on image contents (low-level features, such as colour and texture). They have their respective strengths and weaknesses. In this paper, we describe how to improve text-based and content-based image retrieval techniques and to integrate these two types of techniques in one system to take advantage of their complementing strengths. Our experimental results show that the integrated approach has higher retrieval performance than either the text-based or the content-based techniques used by themselves.

Image Information Retrieval: An Overview of Current Research

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, teachers, artists, and advertising agencies. Image needs and uses across users in these groups vary considerably. Users may require access to images based on primitive features such as color, texture or shape or users may require access to images based on abstract concepts and symbolic imagery. The technology to access these images has also accelerated phenomenally and at present surpasses our understanding of how users interact with visual information. 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. Text-Based Image Retrieval Research Most existing IR systems are text-based, but images frequently have little or no accompanying textual information. The solution historically has been to develop text-based ontologies and classification schemes for image description. Text-based indexing has many strengths including the ability to represent both general and specific instantiations of an object at varying levels of complexity. Reviews of the literature pertaining primarily to text-based approaches include Rasmussen (1997) Lancaster (1998) Lunin (1987) and Cawkell (1993). Long before images could be digitized, access to image collections was provided by librarians, curators, and archivists through text descriptors or classification codes. These indexing schemes were often developed in-house and reflect the unique characteristics of a particular collection or clientele. This is still common practice and recently Zheng (1999) and Goodrum & Martin (1997) have reported on the hybridization of multiple schemas for classifying collections of historic costume collections. Hourihane (1989) has also reviewed a number of these unique systems for image classification. To date, very little research has been conducted on the relative effectiveness of these various approaches to image indexing in electronic environments. Attempts to provide general systems for image indexing include the Getty's Art and Architecture Thesaurus (AAT), which consists of over 120,000 terms for the description of art, art history, architecture, and other cultural objects, and the Library of Congress Thesaurus of Graphic Materials (LCTGM). The AAT currently provides access to thirty-three hierarchical categories of image description using seven broad facets (Associated Concepts, Physical Attributes, Styles and Periods, Agents, Activities, Materials, and Objects). The approach in many collections, particularly general library environments, has been to apply an existing cataloging system like the Dewey Decimal System to image description using the LCTGM, or ICONCLASS. Assignment of terms to describe images is not solved entirely by the use of controlled vocabularies or classification schemes however. The textual representation of images is problematic because images convey information relating to what is actually depicted in the image as well as what the image is about. Shatford (1986) posits this discussion within a framework based on Panofsky's approach to analyzing iconographical levels of meaning in images (1955). …

Color and spatial feature for content-based image retrieval

Most of the currently available image database systems provide a text-based retrieval function called keyword retrieval, where users specify `keywords' such as titles, attributes, and categories of themes. But many times it is not easy for users to specify suitable keywords for a particular retrieval. Besides, building a large image database with complete description of contents is a very difficult task. In this paper, we present a content-based retrieval method which obviates the need to describe certain contents of an image to be archived and retrieved. The proposed method computes image features automatically from a given image and they can be used to archive and/or retrieve images. These features are based on color and its spatial distribution information in an image. We have also developed a similarity measure to compare the color and spatial feature similarity of two images. This technique has been developed and tested for content-based similarity retrieval of images on two databases consisting of: (i) 100 test images and (ii) 800 actual trademarks images. The experimental results show a high efficiency of retrieval.

SHAPE-BASED RETRIEVAL: A CASE STUDY WITH TRADEMARK IMAGE DATABASES

Retrieval efficiency and accuracy are two important issues in designing a content-based database retrieval system. We propose a method for trademark image database retrieval based on object shape information that would supplement traditional text-based retrieval systems. This system achieves both the desired efficiency and accuracy using a two-stage hierarchy: in the first stage, simple and easily computable shape features are used to quickly browse through the database to generate a moderate number of plausible retrievals when a query is presented; in the second stage, the candidates from the first stage are screened using a deformable template matching process to discard spurious matches. We have tested the algorithm using hand drawn queries on a trademark database containing 1100 images. Each retrieval takes a reasonable amount of computation time (∼4–5 s on a Sun Space 20 workstation). The topmost image retrieved by the system agrees with that obtained by human subjects, but there are significant differences between the ranking of the top-10 images retrieved by our system and the ranking of those selected by the human subjects. This demonstrates the need for developing shape features that are better able to capture human perceptual similarity of shapes. An improved heuristic has been suggested for more accurate retrievals. The proposed scheme matches filled-in query images against filled-in images from the database, thus using only the gross details in the image. Experiments with database images used as query images have shown that matching on the filled-in database extracts more images within the top-20 retrievals that have similar content. We believe that developing an automatic retrieval algorithm which matches human performance is an extremely difficult and challenging task. However, considering the substantial amount of time and effort needed for a manual retrieval from a large image database, an automatic shape-based retrieval technique can significantly simplify the retrieval task.

Personal Document Management and Retrieval: A Knowledge-Based Approach

This paper presents a knowledge-based approach to managing and retrieving personal documents. The dual document models consist of a document type hierarchy and a folder organization. The document type hierarchy is used to capture the layout, logical and conceptual structures of documents. The folder organization mimics the user's real-world document filing system for organizing and storing documents in an office environment. Predicate-based representation of documents is formalized for specifying knowledge about documents. Document filing and retrieval are predicate-driven. The filing criteria for the folders, which are specified in terms of predicates, govern the grouping of frame instances, regardless of their document types. We incorporated the notions of document type hierarchy and folder organization into the multilevel architecture of document storage. This architecture supports various text-based information retrieval techniques and content-based multimedia information retrieval techniques. The paper also proposes a knowledge-based query-preprocessing algorithm, which reduces the search space. For automating the document filing and retrieval, a predicate evaluation engine with a knowledge base is proposed. The learning agent is responsible for acquiring the knowledge needed by the evaluation engine.