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Content Based Image Retrieval
Content-based image retrieval (CBIR) systems demonstrate excellent performance at computing low-level features from pixel representations but its output does not reflect the overall desire of the user. The systems perform poorly in extracting high-level (semantic) features that include objects and their meanings, actions and feelings. This phenomenon, referred to as the semantic gap, has necessitated current research in CBIR systems towards retrieving images by the type of object or scene depicted.
Content-based image retrieval (CBIR), also known as query by image content (QBIC) and content-based visual information retrieval (CBVIR) is the application of computer vision to the image retrieval problem, that is, the problem of searching for digital images in large databases
Content-based" means that the search will analyze the actual contents of the image. The term 'content' in this context might refer colors, shapes, textures, or any other information that can be derived form the image itself. Without the ability to examine image content, searches must rely on metadata such as captions or keywords. Such metadata must be generated by a human and stored alongside each image in the database
In literature the term content based image retrieval (CBIR) has been used for the first time by Kato et.al. , to describe his experiments into automatic retrieval of images from a database by color and shape feature. The typical CBIR system
performs two major tasks [16,17]. The first one is feature extraction (FE), where a set of features, called feature vector, is generated to accurately represent the content of each image in the database
1946, Dennis Gabor,the 1971 Nobel prize winner in Holography was,like every other scientist,interested in the problem of obtaining simultaneous localization in both time/space and frequency domains.He was motivated by developments in quantum mechanics including Heisenberg's uncertainty principle, and the fundamental results of Nyquist and Hartley on the limits for the transmission of information over a channel
The experimental results have shown that the colour averaging techniques outperform the CBIR technique using all pixel data. In generic image database forward diagonal mean gives highest precision and recall crossover value indicating best performance and all other proposed techniques perform better than all pixel data. Howsoever it is observed that the image tiling does not helps in further improvement of retrieval accuracy. The difficult task of improving the performance of content based image retrieval techniques with reduction in time complexity is achieved here with help of proposed colour averaging based CBIR techniques.
With the widespread dissemination of picture archiving and communication systems (PACSs) in hospitals, the amount of imaging
data is rapidly increasing. Effective image retrieval systems are required to manage these complex and large image databases. The authors reviewed the past development and the present state of medical image retrieval systems including textbased and content-based systems. In order to provide a more effective image retrieval service, the intelligent content-based retrieval systems combined with semantic systems are required
Ahmed Ismail sihab (Department of Computing, Imperial College of Science, Technology and Medicine,University of London), “FUZZY CLUSTERING ALGORITHMS AND THEIR APPLICATION TO MEDICAL IMAGE ANALYSIS”.
Eakins JP. Towards intelligent image retrieval. Pattern Recogn 2002;35:3-14
Ma WY, Manjunath BS. NeTra: a toolbox for navigating large image databases. In: Proceedings of International Conference on Image Processing, 1997. p.568-71.
Minh N. Do, Martin Vetterli, “Wavelet-Based Texture Retrieval Using Generalized Gaussian Density and Kullback-Leibler Distance”, IEEE Transactions On Image Processing, Volume 11, Number 2, pp.146-158, February 2002.
Pentland A, Picard RW, Sclaroff S. Photobook: contentbased manipulation of image databases. Int J Comput Vis 1996;18:233-54.
W. Jiang, G. Er, Q. Dai and J. Gu, “Similarity‐Based Online Feature Selection in Content‐Based Image Retrieval”, IEEE Trans. on Image Processing, vol. 15, no. 3, pp. 101‐104, March
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