Content-based image retrieval

About: Content-based image retrieval is a research topic. Over the lifetime, 6916 publications have been published within this topic receiving 150696 citations. The topic is also known as: CBIR.

Content based Image Retrieval for Medical Images using Canny Edge Detection Algorithm

Abstract: The rapid expansion of digital data content has led to the need for rich descriptions and efficient Retrieval Tool. To develop this, content based image Retrieval method has played an important role in the field of image retrieval. This paper aims to provide an efficient medical image data Retrieval from a huge content of medical database using one of the images content such as image shape, because, efficient content-based image Retrieval in the medical domain is still a challenging problem. The main objective of this paper is to provide an efficient tool which is used for efficient medical image retrieval from a huge content of medical image database and which is used for further medical diagnosis purposes.

Computer-Aided Diagnosis in Mammography Using Content-based Image Retrieval Approaches: Current Status and Future Perspectives.

Abstract: As the rapid advance of digital imaging technologies, the content-based image retrieval (CBIR) has became one of the most vivid research areas in computer vision. In the last several years, developing computer-aided detection and/or diagnosis (CAD) schemes that use CBIR to search for the clinically relevant and visually similar medical images (or regions) depicting suspicious lesions has also been attracting research interest. CBIR-based CAD schemes have potential to provide radiologists with “visual aid” and increase their confidence in accepting CAD-cued results in the decision making. The CAD performance and reliability depends on a number of factors including the optimization of lesion segmentation, feature selection, reference database size, computational efficiency, and relationship between the clinical relevance and visual similarity of the CAD results. By presenting and comparing a number of approaches commonly used in previous studies, this article identifies and discusses the optimal approaches in developing CBIR-based CAD schemes and assessing their performance. Although preliminary studies have suggested that using CBIR-based CAD schemes might improve radiologists’ performance and/or increase their confidence in the decision making, this technology is still in the early development stage. Much research work is needed before the CBIR-based CAD schemes can be accepted in the clinical practice.

A Comparison of Shape Retrieval Using Fourier Descriptors and Short-Time Fourier Descriptors

Abstract: Shape is one of the primary features in Content Based Image Retrieval (CBIR). Many shape representations and retrieval methods exist. However, most of those methods either do not well capture shape features or are difficult to do normalization (or matching). Among them, methods based Fourier descriptors (FDs) achieve both good representation and easy normalization. FDs is often blamed for not being able to locate local shape features. Methods are proposed in attempt to overcome this drawback. These methods include short-time Fourier transform and wavelet transform. In this paper, we make a study and a comparison of shape retrieval using FDs and short-time Fourier descriptors (SFDs). Query data is given to show the retrieval performance of this two descriptors on a standard database.

Content-based Image Retrieval Using Gabor-Zernike Features

Abstract: Content-based image retrieval (CBIR) is an important research area for manipulating large amount of image databases and archives. Extraction of invariant features is the basis of CBIR. This paper focuses on the problem of texture and shape feature extractions. We investigate texture feature and shape feature for CBIR by successfully combining the Gabor filters and Zernike moments (GF+ZM). GF is used for texture feature extraction and ZM extracts shape features. Comprehensive performance evaluation of our method is based on three different databases: face database, fingerprint database, and MPEG-7 shape database. The experimental results demonstrate that GF+ZM presents robustness to all of the three databases with the best average retrieval rate while the GF and ZM are limited for certain databases. GF is effective for face database and fingerprint database but is weak for MPEG-7 shape database. ZM achieves high retrieval rate for face database and MPEG-7 shape database but gives relatively low retrieval rate for fingerprint database.