Content-Based Retrieval Concept

Content-Based Retrieval Concept

Yung-Kuan Chan (National Chung Hsing University, Taiwan, R.O.C.) and Chin-Chen Chang (National Chung Cheng University, Taiwan, R.O.C.)
DOI: 10.4018/978-1-60566-026-4.ch122
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Abstract

Because of the demand for efficient management in images, much attention has been paid to image retrieval over the past few years. The text-based image retrieval system is commonly used in traditional search engines (Ratha et al., 1996), where a query is represented by keywords that are usually identified and classified by human beings. Since people have different understandings on a particular image, the consistency is difficult to maintain. When the database is larger, it is arduous to describe and classify the images because most images are complicated and have many different objects. There has been a trend towards developing the content-based retrieval system, which tries to retrieve images directly and automatically based on their visual contents. A similar image retrieval system extracts the content of the query example q and compares it with that of each database image during querying. The answer to this query may be one or more images that are the most similar ones to q. Similarity retrieval can work effectively when the user fails to express queries in a precise way. In this case, it is no longer necessary to retrieve an image extremely similar to the query example. Hence, similarity retrieval has more practical applications than an exact match does.
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Introduction

Because of the demand for efficient management in images, much attention has been paid to image retrieval over the past few years. The text-based image retrieval system is commonly used in traditional search engines (Ratha et al., 1996), where a query is represented by keywords that are usually identified and classified by human beings. Since people have different understandings on a particular image, the consistency is difficult to maintain. When the database is larger, it is arduous to describe and classify the images because most images are complicated and have many different objects. There has been a trend towards developing the content-based retrieval system, which tries to retrieve images directly and automatically based on their visual contents.

A similar image retrieval system extracts the content of the query example q and compares it with that of each database image during querying. The answer to this query may be one or more images that are the most similar ones to q. Similarity retrieval can work effectively when the user fails to express queries in a precise way. In this case, it is no longer necessary to retrieve an image extremely similar to the query example. Hence, similarity retrieval has more practical applications than an exact match does.

Content-Based Image Retrieval Systems

In a typical content-based image retrieval system, the query pattern is queried by an example in which a sample image or sketch is provided. The system then extracts appropriate visual features that can describe the image, and matches these features against the features of the images stored in the database. This type of query is easily expressed and formulated, since the user does not need to be familiar with the syntax of any special purpose image query language. The main advantage is that the retrieval process can be implemented automatically (Chen, 2001). The scope of this article is circumscribed to image abstraction and retrieval based on image content.

Human beings have a unique ability that can easily recognize the complex features in an image by utilizing the attributes of shape, texture, color, and spatial information. Many researchers analyze the color, texture, shape of an object, and spatial attributes of images, and use them as the features of the images. Therefore, one of the most important challenges in building an image retrieval system is the choice and representation of the visual attributes. A brief overview of the commonly used visual attributes shape, texture, color, and spatial relationship will be illustrated as follows.

Key Terms in this Chapter

This work was previously published in Encyclopedia of Information Science and Technology: edited by M. Khosrow-Pour, pp. 564-568, copyright 2005 by Information Science Reference, formerly known as Idea Group Reference (an imprint of IGI Global)

Query by Example: The image retrieval system where a sample image or sketch can be provided as a query.

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