Results 191 to 200 of about 4,123 (220)

A density clustering approach for CBIR system

2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA), 2016
Searching an image in a huge set of images became an important task in several domains such as crime, medicine, geology and so on. The task of retrieving images by their visual contents is called content-based image retrieval (CBIR) systems. These systems have to be fast, efficient and semantically similar.
Lacheheb Hadjer, Saliha Aouat
openaire   +1 more source

A Comparison of Relevance Feedback Strategies in CBIR

6th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2007), 2007
Relevance feedback (RF) is considered to be very useful in reducing semantic gap and thus enhancing accuracy of a Content-Based Image Retrieval system. In this paper, we have given a brief overview of research done in this area with an emphasis on feature re-weighting approach, a popular RF technique.
Gita Das, Sid Ray
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A new approach for texture classification in CBIR

International Journal of Computer Applications in Technology, 2010
In the field of Content-Based Image Retrieval (CBIR), the semantic understanding of textures has long been a difficult problem, especially the texture classification. This paper proposes a new approach for texture classification, which adopts ten words describing textures in natural language.
Shengju Sang, Mingxia Liu 0001, Jing Liu, Qi An   +3 more
openaire   +1 more source

Saliency and Burstiness for Feature Selection in CBIR

2019 8th European Workshop on Visual Information Processing (EUVIP), 2019
The paper addresses the problem of visual feature selection in content-based image retrieval (CBIR). We propose to study two strategies: the first one is using visual saliency, that selects the most salient features of the image and the second one exploits burstiness, that detects and processes the repeated visual elements in the image.
Kamel Guissous, Valérie Gouet-Brunet
openaire   +1 more source

Behaviour of Texture Features in a CBIR System

2008
Searching and processing in databases of general and non-specific images are highly subjective. The process of texture feature extraction from images produces results of highly theoretical and mathematical character that have little to do with human perception.
César Reyes, Maria Luisa Durán, Teresa Alonso, Pablo García Rodríguez, Andrés Caro   +4 more
openaire   +1 more source

A web-based evaluation system for CBIR

Proceedings of the 2001 ACM workshops on Multimedia multimedia information retrieval - MULTIMEDIA '01, 2001
This papers describes a benchmark test for content-based image retrieval systems (CBIRSs) with the query by example (QBE) query paradigm. This benchmark is accessible via the Internet and thus allows to evaluate any CBIRS which is compliant with Multimedia Retrieval Markup Language (MRML) for query formulation and result transmission.
Henning Müller, Wolfgang Müller 0001, David McG. Squire   +2 more
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Efficient and Flexible Cluster-and-Search for CBIR

2008
Content-Based Image Retrieval is a challenging problem both in terms of effectiveness and efficiency. In this paper, we present a flexible cluster-and-search approach that is able to reuse any previously proposed image descriptor as long as a suitable similarity function is provided.
Anderson Rocha 0001, Jurandy Almeida, Mario A. Nascimento, Ricardo da Silva Torres, Siome Goldenstein   +4 more
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About the Embedding of Color Uncertainty in CBIR Systems

2007
This paper focuses on the embedding of the uncertainty about color images, naturally arising from the quantization and the human perception of colors, into histogram-type descriptors, adopted as indexing mechanism. In particular, our work has led to an extension of the GIFT platform for Content Based Image Retrieval based on fuzzy color indexing in the
Di Donna Fabio, Maddalena Lucia, Petrosino Alfredo   +2 more
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Investigating CBIR techniques for cervicographic images.

AMIA ... Annual Symposium proceedings. AMIA Symposium, 2008
The National Library of Medicine (NLM) and the National Cancer Institute (NCI) are creating a digital archive of 100,000 cervicographic images and clinical and diagnostic data obtained through two major longitudinal studies. In addition to developing tools for Web access to these data, we are conducting research in Content-Based Image Retrieval (CBIR ...
Zhiyun Xue, Sameer K. Antani, L. Rodney Long, Jose Jeronimo, George R. Thoma   +4 more
openaire   +2 more sources

Content-Based Image Retrieval (CBIR): A Review

Lecture Notes in Electrical Engineering, 2022
Sharma Dilip Kumar
exaly