Content-based image retrieval (CBIR) is a promising technology to enrich the core functionality of picture archiving and communication systems (PACS). CBIR has a potentially strong impact in diagnostics, research, and education. Research successes that are increasingly reported in the scientific literature, however, have not made significant inroads as medical CBIR applications incorporated int...

A major challenge in Content-Based Image Retrieval (CBIR) is to bridge the semantic gap between low-level image contents and high-level semantic concepts. Although researchers have investigated a variety of retrieval techniques using different types of features and distance functions, no single best retrieval solution can fully tackle this challenge. In a real-world CBIR task, it is often highl...

In this study, we propose a fuzzy logic CBIR (content-based image retrieval) system for finding textures. In our CBIR system, a user can submit textual descriptions and/or visual examples to find the desired textures. After the initial search, the user can give relevant and/or irrelevant examples to refine the query and improve the retrieval efficiency. Contributions of this study are fourfold....

Content-Based Image Retrieval (CBIR) technology has been proposed to benefit not only the management of increasingly large medical image collections, but also to aid clinical care, biomedical research, and education. Based on a literature review, we conclude that there is widespread enthusiasm for CBIR in the engineering research community, but the application of this technology to solve practi...

From a human centered perspective three ingredients for Content-Based Image Retrieval (CBIR) were developed. First, with their existence confirmed by experimental data, 11 color categories were utilized for CBIR and used as input for a new color space segmentation technique. The complete HSI color space was divided into 11 segments (or bins), resulting in a unique CBIR 11 color quantization sch...

Content-Based Image Retrieval (CBIR) systems are powerful search tools in image databases that have been little applied to hyperspectral images. Relevance Feedback (RF) is an iterative process that uses machine learning techniques and user’s feedback to improve the CBIR systems performance. We pursued to expand previous research in hyperspectral CBIR systems built on dissimilarity functions def...

Comparing the performance of CBIR (Content-Based Image Retrieval) algorithms is difficult. Private data sets are used, so it is controversial to compare CBIR algorithms developed by different researchers. Also, the performance of CBIR algorithms is usually measured on an isolated, well-tuned PC or workstation. In a realworld environment, however, the CBIR algorithms would only constitute a mino...

Content-Based Image Retrieval (CBIR) systems are required to effectively harness information from ubiquitous image collections. Despite intense research efforts by the multidisciplinary CBIR community since early 1990s, apparently there is a mismatch between these advances and the one that is truly required to bring success to CBIR in the commercial market place. In this paper we provide an ove...

The paper presents the architecture of experimental Content-Based Image Retrieval (CBIR) system APICAS ("Art Painting Image Colour Aesthetics and Semantics"). This system has been developed within a doctoral thesis which aims to provide a suite of specialized tools for CBIR within a digital library of art images. The high-level architecture suggested in this work takes OAIS as a basis and adds ...

The performance evaluation of Content Based Image Retrieval systems (CBIR), can be considered as a challenging and overriding problem even for human expert users regarding the important numbers CBIR proposed in literature applied to different image databases. automatic measures widely used assess are inspired from general Text (TR) domain such precision recall metrics. This paper proposes new q...