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Content-based image retrieval

About: Content-based image retrieval is a research topic. Over the lifetime, 6916 publications have been published within this topic receiving 150696 citations. The topic is also known as: CBIR.


Papers
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Journal ArticleDOI
TL;DR: Experimental results demonstrate the usefulness and effectiveness of the proposed similarity measure for image retrieval, which not only takes into consideration the perceptual similarity between images, but also significantly improves the retrieval performance of the Euclidean distance measure.
Abstract: A new scheme of learning similarity measure is proposed for content-based image retrieval (CBIR). It learns a boundary that separates the images in the database into two clusters. Images inside the boundary are ranked by their Euclidean distances to the query. The scheme is called constrained similarity measure (CSM), which not only takes into consideration the perceptual similarity between images, but also significantly improves the retrieval performance of the Euclidean distance measure. Two techniques, support vector machine (SVM) and AdaBoost from machine learning, are utilized to learn the boundary. They are compared to see their differences in boundary learning. The positive and negative examples used to learn the boundary are provided by the user with relevance feedback. The CSM metric is evaluated in a large database of 10009 natural images with an accurate ground truth. Experimental results demonstrate the usefulness and effectiveness of the proposed similarity measure for image retrieval.

227 citations

Book ChapterDOI
TL;DR: This contribution develops a new technique for content-based image retrieval that classify the images based on local invariants that represent the image in a very compact way and allow fast comparison and feature matching with images in the database.
Abstract: This contribution develops a new technique for content-based image retrieval. Where most existing image retrieval systems mainly focus on color and color distribution or texture, we classify the images based on local invariants. These features represent the image in a very compact way and allow fast comparison and feature matching with images in the database. Using local features makes the system robust to occlusions and changes in the background. Using invariants makes it robust to changes in viewpoint and illumination.

223 citations

Journal ArticleDOI
TL;DR: The results of the experiments show that the MPEG-7-defined content descriptors can be used as such in thePicSOM system even though Euclidean distance calculation, inherently used in the PicSom system, is not optimal for all of them.
Abstract: Development of content-based image retrieval (CBIR) techniques has suffered from the lack of standardized ways for describing visual image content. Luckily, the MPEG-7 international standard is now emerging as both a general framework for content description and a collection of specific agreed-upon content descriptors. We have developed a neural, self-organizing technique for CBIR. Our system is named PicSOM and it is based on pictorial examples and relevance feedback (RF). The name stems from "picture" and the self-organizing map (SOM). The PicSOM system is implemented by using tree structured SOMs. In this paper, we apply the visual content descriptors provided by MPEG-7 in the PicSOM system and compare our own image indexing technique with a reference system based on vector quantization (VQ). The results of our experiments show that the MPEG-7-defined content descriptors can be used as such in the PicSOM system even though Euclidean distance calculation, inherently used in the PicSOM system, is not optimal for all of them. Also, the results indicate that the PicSOM technique is a bit slower than the reference system in starting to find relevant images. However, when the strong RF mechanism of PicSOM begins to function, its retrieval precision exceeds that of the reference system.

222 citations

Proceedings ArticleDOI
01 Jan 1992
TL;DR: In this paper, the QVE (Query by Visual Example) system is proposed to evaluate the similarity between the rough sketch and each of the image data in the database automatically, which is quite effective for content based image retrieval.
Abstract: Gives a basic idea and its fundamental algorithms of the visual interface for image database systems. The QVE (Query by Visual Example) accepts a sketch roughly drawn by a user to retrieve the original image and the similar images. The system evaluates the similarity between the rough sketch, i.e. a visual example, and each of the image data in the database automatically. The QVE interface is implemented and examined on an experimental electronic art gallery called ART MUSEUM. This paper also gives some experimental results and a current evaluation. The algorithms are quite effective for content based image retrieval. >

219 citations

Journal ArticleDOI
TL;DR: This paper proposes a novel computational visual attention model, namely saliency structure model, for content-based image retrieval, and introduces a novel visual cue, namely color volume, with edge information together, to detect saliency regions.

218 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202358
2022141
2021180
2020163
2019224
2018270