Combining Diversity Queries and Visual Mining to Improve Content-Based Image Retrieval Systems: The DiVI Method

2015 ◽  
Author(s):  
Lucio F.D. Santos ◽  
Rafael L. Dias ◽  
Marcela X. Ribeiro ◽  
Agma J.M. Traina ◽  
Caetano Traina
Keyword(s):  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
Retrieval Systems

Issues and Techniques to Mitigate the Performance Gap in Content-Based Image Retrieval Systems

2011 ◽  
pp. 60-83
Author(s):  
Agma J. M. Traina ◽  
Caetano Traina ◽  
Robson Cordeiro ◽  
Marcela Ribeiro ◽  
Paulo M. Azevedo-Marques
Keyword(s):  
Image Retrieval ◽  
Poor Quality ◽  
Content Based Image Retrieval ◽  
Performance Gap ◽  
Similarity Queries ◽  
Access Methods ◽  
Retrieval Systems ◽  
Computational Support ◽  
Indexing And Retrieval ◽  
Key Aspects

This chapter discusses key aspects concerning the performance of Content-based Image Retrieval (CBIR) systems. The so-called performance gap plays an important role regarding the acceptability of CBIR systems by the users. It provides a timely answer to the actual demand for computational support from CBIR systems that provide similarity queries processing. Focusing on the performance gap, this chapter explains and discusses the main problems currently under investigation: the use of many features to represent images, the lack of appropriate indexing structures to retrieve images and features, deficient query plans employed to execute similarity queries, and the poor quality of results obtained by the CBIR system. We discuss how to overcome these problems, introducing techniques such as how to employ feature selection techniques to beat the “dimensionality curse” and how to use proper access methods to support fast and effective indexing and retrieval of images, stressing the importance of using query optimization approaches.

A Content-Based Approach to Medical Image Database Retrieval

2011 ◽  
pp. 281-302
Author(s):  
Chia-Hung Wei ◽  
Chang-Tsun Li ◽  
Roland Wilson
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Image Features ◽  
Content Based Image Retrieval ◽  
Database Retrieval ◽  
Retrieval Systems ◽  
Large Databases ◽  
Advance Research ◽  
Intervention Content

Content-based image retrieval (CBIR) makes use of image features, such as color and texture, to index images with minimal human intervention. Content-based image retrieval can be used to locate medical images in large databases. This chapter introduces a content-based approach to medical image retrieval. Fundamentals of the key components of content-based image retrieval systems are introduced first to give an overview of this area. A case study, which describes the methodology of a CBIR system for retrieving digital mammogram database, is then presented. This chapter is intended to disseminate the knowledge of the CBIR approach to the applications of medical image management and to attract greater interest from various research communities to rapidly advance research in this field.

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