Content-based image retrieval using color and texture fused features
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TLDR
This paper presents a method to extract color and texture features of an image quickly for content-based image retrieval (CBIR), and shows that the fused features retrieval brings better visual feeling than the single feature retrieval, which means better retrieval results.About:
This article is published in Mathematical and Computer Modelling.The article was published on 2011-08-01 and is currently open access. It has received 297 citations till now. The article focuses on the topics: Content-based image retrieval & Color histogram.read more
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Department of Computer Science and Engineering
TL;DR: In this article, the authors present a survey of postgraduate students: Vladimír Arnot, Daniel Čapek, Rudolf Čejka, Dao Minh, Tomá Dulík, Martin Hrubý, Radek Kočí, Petr Kotásek, Marek Křejpský and Bohuslav KŘena, Vladislav Kubíček.
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A new matching strategy for content based image retrieval system
TL;DR: An accurate and rapid model for content based image retrieval process depending on a new matching strategy that provides accurate retrieval results and achieve improvement in performance with significantly less computation time compared with other models is introduced.
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A novel content-based image retrieval approach for classification using GLCM features and texture fused LBP variants
Meenakshi Garg,Gaurav Dhiman +1 more
TL;DR: The proposed approach contains the complete structural information extracted from the local binary patterns and also extracts the additional information using the information of magnitude, thereby achieving extra discriminative power.
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Information fusion in content based image retrieval
Luca Piras,Giorgio Giacinto +1 more
TL;DR: A journey through the main information fusion ingredients that a recipe for the design of a CBIR system should include to meet the demanding needs of users is offered.
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Radiological images and machine learning: Trends, perspectives, and prospects.
Zhenwei Zhang,Ervin Sejdic +1 more
TL;DR: The fundamental concepts behind various machine learning techniques and their applications in several radiological imaging areas are covered, such as medical image segmentation, brain function studies and neurological disease diagnosis, as well as computer-aided systems, image registration, and content-based image retrieval systems.
References
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Journal ArticleDOI
Content-based image retrieval at the end of the early years
TL;DR: The working conditions of content-based retrieval: patterns of use, types of pictures, the role of semantics, and the sensory gap are discussed, as well as aspects of system engineering: databases, system architecture, and evaluation.
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Image retrieval: Ideas, influences, and trends of the new age
TL;DR: Almost 300 key theoretical and empirical contributions in the current decade related to image retrieval and automatic image annotation are surveyed, and the spawning of related subfields are discussed, to discuss the adaptation of existing image retrieval techniques to build systems that can be useful in the real world.
Proceedings ArticleDOI
QBIC project: querying images by content, using color, texture, and shape
Carlton Wayne Niblack,R. Barber,Will Equitz,Myron D. Flickner,Eduardo H. Glasman,Dragutin Petkovic,Peter Cornelius Yanker,Christos Faloutsos,Gabriel Taubin +8 more
TL;DR: The main algorithms for color texture, shape and sketch query that are presented, show example query results, and discuss future directions are presented.
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SIMPLIcity: semantics-sensitive integrated matching for picture libraries
TL;DR: SIMPLIcity (semantics-sensitive integrated matching for picture libraries), an image retrieval system, which uses semantics classification methods, a wavelet-based approach for feature extraction, and integrated region matching based upon image segmentation to improve retrieval.
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Relevance feedback: a power tool for interactive content-based image retrieval
TL;DR: A relevance feedback based interactive retrieval approach that effectively takes into account the subjectivity of human perception of visual content and the gap between high-level concepts and low-level features in CBIR.