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Faceted Content-Based Image Retrieval
2008 19th International Conference on Database and Expert Systems Applications, 2008In typical content-based image retrieval systems it is not possible to navigate the image space by simultaneously applying multiple similarity criteria. The model we propose addresses this problem by representing the search for the images similar to a given image as the exploration of a lattice of (non-disjoint) image clusters, induced by a natural ...
Amato G, Meghini C
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Content based image and video retrieval
SPIE Proceedings, 2010The growing capacity of computers, the abundance of digital cameras and the increased connectivity of the world all point to large digital multimedia archives. They include images and videos from the World Wide Web, museum objects, flowers, trademarks, and views from everyday life.
Shubhangi H. Patil +4 more
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SPIE Proceedings, 2010
Large collection of information is being created in many areas of modern life on daily basis. This information exists in many forms from plain text to high resolution multimedia. Today computers are many times faster than human in text based searching using keywords and indexing but the story is totally different in case of multimedia.
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Large collection of information is being created in many areas of modern life on daily basis. This information exists in many forms from plain text to high resolution multimedia. Today computers are many times faster than human in text based searching using keywords and indexing but the story is totally different in case of multimedia.
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Content Based Image Retrieval with Hadoop
2016Hadoop has become a widely used open source framework for large scale data processing. MapReduce is the core component of Hadoop. It is this programming paradigm that allows for massive scalability across hundreds or thousands of servers in a Hadoop cluster. It allows processing of extremely large video files or image files on data nodes.
Heba Gaber +3 more
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A content-based image retrieval system
Image and Vision Computing, 1998Abstract This paper proposes a Content-Based Images Retrieval (CBIR) system which uses a modified geometric hashing technique to retrieve similar shape images from the image database. The CBIR system is a two-stage image retrieval system: the outline-based image retrieval and the hash-table-based image retrieval.
Chung-Lin Huang, Dai-Hwa Huang
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Content Based Image Retrieval Technique
2008A retrieval methodology which integrates color, texture and shape information is presented in this paper. Consequently, the overall image similarity is developed through the similarity based on all the feature components. Alternatively to known CBIR systems, we compute features only in the finite number of extracted ROIs.
Ryszard S. Choras +2 more
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Content-based retrieval of ophthalmological images
Proceedings of 3rd IEEE International Conference on Image Processing, 2002This paper describes steps towards an information system for the storage and content-based retrieval of ocular fundus images. Based on the Virage Incorporated framework for defining similarity metrics, the authors have developed a number of primitives for the representation of ocular fundus images.
Amarnath Gupta +6 more
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A review of content-based image retrieval
2010 7th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP 2010), 2010A comprehensive survey on patch recognition, which is a crucial part of content-based image retrieval (CBIR), is presented. CBIR can be viewed as a methodology in which three correlated modules including patch sampling, characterizing, and recognizing are employed.
Gholamreza Rafiee +2 more
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Content-based retrieval of segmented images
Proceedings of the second ACM international conference on Multimedia - MULTIMEDIA '94, 1994Most general content-based image retrieval techniques use colour and texture as main retrieval indices. A recent technique uses colour pairs to model distinct object boundaries for retrieval. These techniques have been applied to overall image contents without taking into account the characteristics of individual objects. While the techniques work well
Tat-Seng Chua, S.-K. Lim, Hung Keng Pung
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An introduction to content-based image retrieval
Eighth International Conference on Digital Information Management (ICDIM 2013), 2013Content-based image retrieval (CBIR) extracts features from images to support image search. In this tutorial paper, we will review some of the basic CBIR algorithms that derive colour, texture and shape features, and will then show how image features can be extracted from the compressed domain, in particular from JPEG images, without the need of ...
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