Topic
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 published on a yearly basis
Papers
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16 May 2012
TL;DR: It is found that further modifications are needed to produce better performance in searching images, and cross correlation value & image descriptor attributes are calculated prior histogram implementation to make CBIR system more efficient.
Abstract: The need for efficient content-based image retrieval system has increased hugely. Efficient and effective retrieval techniques of images are desired because of the explosive growth of digital images. content based image retrieval (CBIR) is a promising approach because of its automatic indexing retrieval based on their semantic features and visual appearance. The similarity of images depends on the feature representation.However users have difficulties in representing their information needs in queries to content based image retrieval systems. In this paper we investigate two methods for describing the contents of images. The first one characterizes images by global descriptor attributes, while the second is based on color histogram approach.To compute feature vectors for Global descriptor, required time is much less as compared to color histogram. Hence cross correlation value & image descriptor attributes are calculated prior histogram implementation to make CBIR system more efficient.The performance of this approach is measured and results are shown. The aim of this paper is to compare various global descriptor attributes and to make CBIR system more efficient. It is found that further modifications are needed to produce better performance in searching images.
89 citations
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11 Aug 2003TL;DR: In this article, a relevance feedback approach that uses positive examples to perform generalization and negative example to perform specialization is described, where a query containing both positive and negative examples is processed in two general steps.
Abstract: Although negative example can be highly useful to better understand the user's needs in content-based image retrieval, it was considered by few authors. A content-based image retrieval method according to the present invention addresses some issues related to the combination of positive and negative examples to perform a more efficient image retrieval. A relevance feedback approach that uses positive example to perform generalization and negative example to perform specialization is described herein. In this approach, a query containing both positive and negative example is processed in two general steps. The first general step considers positive example only in order to reduce the set of images participating in retrieval to a more homogeneous subset. Then, the second general step considers both positive and negative examples and acts on the images retained in the first step. Mathematically, relevance feedback is formulated as an optimization of intra and inter variances of positive and negative examples.
89 citations
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TL;DR: An extremely fast CBIR system which uses Multiple Support Vector Machines Ensemble is proposed which has used Daubechies wavelet transformation for extracting the feature vectors of images.
Abstract: Highlights? Effective CBIR for non-texture images. ? An extremely fast CBIR system which uses Multiple Support Vector Machines Ensemble. ? Using Daubechies wavelet transformation for extracting the feature vectors of images. With the evolution of digital technology, there has been a significant increase in the number of images stored in electronic format. These range from personal collections to medical and scientific images that are currently collected in large databases. Many users and organizations now can acquire large numbers of images and it has been very important to retrieve relevant multimedia resources and to effectively locate matching images in the large databases. In this context, content-based image retrieval systems (CBIR) have become very popular for browsing, searching and retrieving images from a large database of digital images with minimum human intervention. The research community are competing for more efficient and effective methods as CBIR systems may be heavily employed in serving time critical applications in scientific and medical domains. This paper proposes an extremely fast CBIR system which uses Multiple Support Vector Machines Ensemble. We have used Daubechies wavelet transformation for extracting the feature vectors of images. The reported test results are very promising. Using data mining techniques not only improved the efficiency of the CBIR systems, but they also improved the accuracy of the overall process.
89 citations
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TL;DR: A content-based image retrieval system designed to retrieve mammographies from large medical image database based on breast density, and integrated to the database of the Image Retrieval in Medical Applications (IRMA) project, that provides images with classification ground truth.
88 citations
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TL;DR: This paper presents a novel context-based approach for redefining distances and later re-ranking images aiming to improve the effectiveness of CBIR systems, where distances among images are redefined based on the similarity of their ranked lists.
88 citations