Results 61 to 70 of about 6,778 (208)
Ranking method for optimizing precision/recall of content-based image retrieval [PDF]
, 2009 The ranking method is a key element of Content-based Image Retrieval (CBIR) system, which can affect the final retrieval performance. In the literature, previous ranking methods based on either distance or probability do not explicitly relate to ...
Ye, Lei, Zhang, Jun
core +3 more sources
Expert Systems, Volume 42, Issue 9, September 2025.ABSTRACT Finding the correct match to a probe image from a vast amount of data is critical for the online retrieval of apparel images. These images are captured under an uncontrolled environment (e.g., viewpoint and illumination changes); therefore, such type of data is extremely challenging in Content‐Based Image Retrieval (CBIR) research.
Marryam Murtaza +5 more
wiley +1 more source
A Systematic Mapping Study of the Metrics, Uses and Subjects of Diversity‐Based Testing Techniques
Software Testing, Verification and Reliability, Volume 35, Issue 2, March 2025.This paper is a systematic mapping study of diversity‐based testing (DBT) techniques that summarizes the key aspects and trends of 167 papers. The study reports the use of 79 similarity metrics with 22 types of software artefacts, which researchers have used to tackle 11 types of software testing problems.
Islam T. Elgendy +2 more
wiley +1 more source
Content-Based Image Retrieval (CBIR) in Big Histological Image Databases
Medical Technologies Journal, 2020 Background: Automatic analysis of Histopathological Images (HIs) demands image processing and Computational Intelligence (CI) techniques. Both Computer-Aided Diagnosis (CAD) and Content-Based Image-Retrieval (CBIR) systems assist diagnosis, disease discovery, and biological decision-making.
openaire +2 more sources
Self-feedback image retrieval algorithm based on annular color moments
EURASIP Journal on Image and Video Processing, 2019 Content-based image retrieval (CBIR) extracts visual content features (such as color, texture, and shape) of a sample image to retrieve another similar image. Due to the existence of the semantic gap, retrieval results are often unsatisfactory.
Ying Deng, Yuanhui Yu
doaj +1 more source
IET Image Processing, Volume 19, Issue 1, January/December 2025.We present a two‐tier deep learning framework for content‐based image retrieval, combining pixel‐level colour classification with image‐level classification and adaptive feature fusion. The system dynamically optimises structural and semantic similarity weights (alpha and beta) via neural prediction, achieving 0.87 0.99 precision across medical and ...
Aqeel M. Humadi +3 more
wiley +1 more source
AIP AdvancesContent-Based Image Retrieval (CBIR) is essential for retrieving images through visual content comparison, addressing the limitations of traditional keyword-based searches.
Monica Palla, Renu Karra
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The Content-Driven Preprocessor of Images for MPEG-7 Descriptions [PDF]
Journal of Systemics, Cybernetics and Informatics, 2003 An image content-driven (CDP) preprocessor is proposed to activate the right MPEG-7 description tools for the recognized feature contents in one image.
Jiann-Jone Chen +2 more
doaj
Content Based Image Retrieval Using Embedded Neural Networks with Bandletized Regions
Entropy, 2015 One of the major requirements of content based image retrieval (CBIR) systems is to ensure meaningful image retrieval against query images. The performance of these systems is severely degraded by the inclusion of image content which does not contain the
Rehan Ashraf +3 more
doaj +1 more source
A Study on the Channel Expansion VAE for Content-Based Image Retrieval
Applied Sciences, 2022 Content-based image retrieval (CBIR) focuses on video searching with fine-tuning of pre-trained off-the-shelf features. CBIR is an intuitive method for image retrieval, although it still requires labeled datasets for fine-tuning due to the inefficiency ...
Kyounghak Lee +3 more
doaj +1 more source