Improving CBIR systems by integrating semantic features

Brahmi, Djamel; Ziou, Djemel

doi:10.1109/cccrv.2004.1301449

Cited by 16 publications

(8 citation statements)

References 15 publications

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“…The drawback of such systems is that they cannot perform well in un-annotated image databases. Image annotation is not only a subjective matter but also a time taking process [41]. In CBIR methods, texture, color and shape based features are used for searching and retrieving images from large collections of data [42].…”

Section: Medical Image Retrievalmentioning

confidence: 99%

Medical Image Analysis using Convolutional Neural Networks: A Review

et al. 2018

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The science of solving clinical problems by analyzing images generated in clinical practice is known as medical image analysis. The aim is to extract information in an affective and efficient manner for improved clinical diagnosis. The recent advances in the field of biomedical engineering has made medical image analysis one of the top research and development area. One of the reason for this advancement is the application of machine learning techniques for the analysis of medical images. Deep learning is successfully used as a tool for machine learning, where a neural network is capable of automatically learning features. This is in contrast to those methods where traditionally hand crafted features are used. The selection and calculation of these features is a challenging task. Among deep learning techniques, deep convolutional networks are actively used for the purpose of medical image analysis. This include application areas such as segmentation, abnormality detection, disease classification, computer aided diagnosis and retrieval.

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Section: Medical Image Retrievalmentioning

confidence: 99%

Medical Image Analysis using Convolutional Neural Networks: A Review

et al. 2018

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“…So, more and more researchers dedicate themselves on the study of image semantic retrieval [15,19] . Branhmi and Ziou [20] have integrated content-based method with metadata-based method and represented image semantics with vector space model. They retrieved image by creating a correlation matrix through analyzing the probability of the metadata, in order to pointing out the importance of the semantic concepts.…”

Section: Applicationsmentioning

confidence: 99%

Research on methodology of image semantic understanding

2007

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To solve the "semantic gap" problem, image semantic understanding is the key technique. In this paper, firstly analyzes the current situation of image semantic understanding research, which includes some methods of image semantic representation and image semantic extraction, and then it discusses the applications of image semantic understanding, and finally it presents the trend about image semantic understanding.

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“…This type of open-ended task is very hard for computers to perform. Current CBIR systems therefore generally make use of lower-level features [2] like texture, color, and shape even though some systems take advantage of very common higher-level features [3] like faces. Not every CBIR system is generic.…”

Section: Query By Semantic Objectsmentioning

confidence: 99%

Performance Evaluation of Ontology and Fuzzybase CBIR

sandhu¹

2013

ACIJ

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IN THIS PAPER,WE HAVE DONE PERFORMANCE EVALUATION OF ONTOLOGY USING LOW-LEVEL FEATURES LIKECOLOR,TEXTURE AND SHAPE BASEDCBIR,WITH TOPIC SPECIFICCBIR.THE RESULTING ONTOLOGY CAN BE USEDTO EXTRACT THE APPROPRIATE IMAGES FROM THE IMAGE DATABASE.RETRIEVING APPROPRIATE IMAGES FROM ANIMAGE DATABASE IS ONE OF THE DIFFICULT TASKS IN MULTIMEDIA TECHNOLOGY.OUR RESULTS SHOW THAT THEVALUES OF RECALL AND PRECISION CAN BE ENHANCED AND THIS ALSO SHOWS THAT SEMANTIC GAP CAN ALSO BEREDUCED.THE PROPOSED ALGORITHM ALSO EXTRACTS THE TEXTURE VALUES FROM THE IMAGES AUTOMATICALLYWITH ALSO ITS CATEGORY(LIKE SMOOTH,COURSE ETC)AS WELL AS ITS TECHNICAL INTERPRETATIO

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Improving CBIR systems by integrating semantic features

Cited by 16 publications

References 15 publications

Medical Image Analysis using Convolutional Neural Networks: A Review

Medical Image Analysis using Convolutional Neural Networks: A Review

Research on methodology of image semantic understanding

Performance Evaluation of Ontology and Fuzzybase CBIR

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