Image retrieval - Wikipedia, Image retrieval system thesis
Image retrieval system thesis
The earliest commercial CBIR system was developed by IBM and was called QBIC (Query by Image Content).  Recent network and graph based approaches have presented a simple and attractive alternative to existing methods. 
The process of digitization does not in itself make image collections easier to manage. Some form of cataloguing and indexing is still necessary - the only difference being that much of the required information can now potentially be derived automatically from the images themselves. The extent to which this potential is currently being realized is discussed below.
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In a practical set up, the number of images can be very large. On the Internet for example, the number of images can be of the order of millions and is ever growing. Even if the time to compare the query image with a candidate image is very short, the cumulative time needed to compare the query image with all images in the database will be long. It is a drawback of the known system that a user searching for an image in such a large database must wait a long time after having submitted the query image in the system.
This site features the content-based image retrieval research ... You can also visit one of the sites that is currently running our system for real-world image ...
BRISC provides a framework for texture feature extraction and similarity comparison of computed tomography (CT) lung nodule images. It was written in C# .NET using Visual Studio .NET 2005 and is designed to be functional and extensible. To browse this website and/or obtain BRISC, use the links on the left.
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Content-based image retrieval - Wikipedia
Simplicity Research - Content-Based Image Retrieval Project
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image retrieval system thesis
image retrieval system thesis