Fayyad, Usama, Padhraic Smyth, N. Weir, and S. Djorgovski. 1995. Automated analysis and exploration of image databases: results, progress, and challenges. Journal of Intelligent Information Systems, 4:1--19.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....data points and a query point in an m dimensional metric space, find the data point that is closest to the query point. Particular interest has centered on solving this problem in high dimensional spaces, which arise from techniques that approximate (see [15] complex data such as images (e.g. [7, 17, 18, 12, 18, 14, 16, 9, 3]) sequences (e.g. 2, 1] video (e.g. 7] and shapes (e.g. 7, 19, 16, 13] with long feature vectors. Similarity queries are performed by taking a given complex object, approximating it with a high dimensional vector to obtain the query point, and determining the data point closest to it ....

U. M. Fayyad and P. Smyth. Automated analysis and exploration of image databases: Results, progress and challenges. Journal of intelligent information systems, 4(1):7--25, 1995.

....powerful. We begin with PCA (principal component analysis) which is an orthodox mathematical method for the e#cient reduction of dimensionality while retaining maximum variability in the dataset. The applications of PCA to image datasets include face recognition [10] and remote sensing images [11], and in the context of meteorology, PCA is often used with the name EOF (empirical orthogonal function) 12] On the other hand, in shape based approaches, we explicitly represent cloud patterns with mathematical shape models. An example of this approach is a shape decomposition method for ....

U.M. Fayyad, P. Smyth, N. Weir, and S. Djorgovski. Automated analysis and exploration of image databases: Results, progress, and challenges. Journal of Intelligent Information Systems, 4:7-- 25, 1995.

....points and a query point in an m dimensional metric space, find the data point that is closest to the query point. Particular interest has centered on solving this problem in high dimensional spaces, which arise from techniques that approximate (e.g. see [24] complex data such as images (e.g. [15, 28, 29, 21, 29, 23, 25, 18, 3]) sequences (e.g. 2, 1] video (e.g. 15] and shapes (e.g. 15, 30, 25, 22] with long feature vectors. Similarity queries are performed by taking a given complex object, approximating it with a high dimensional vector to obtain the query point, and determining the data point closest to ....

Fayyad, U.M., Smyth, P.: Automated Analysis and Exploration of Image Databases: Results, Progress and Challenges. In Journal of intelligent information systems, Vol. 4, No. 1 (1995) 7--25

....when driving on a variety of road types. Learning to classify new astronomical structures: Machine learning methods have been applied to a variety of large databases to learn general regularities implicit in the data. For example, decision tree learning algorithms have been used by NASA [Fayyad et al. 1995] to learn how to classify celestial objects. 2 Learning to play world class backgammon: The most successful computer programs for playing games such as backgammon [Tesauro, 1995] are based on ML algorithms. 1.1 Inductive Learning Inductive learning is a kind of learning in which given a set ....

Fayyad, U., Smyth, P., Weir, N., and Djorgovski, S. (1995). Automated analysis and exploration of image databases: Results,progress, and challenges. Journal of Intelligent Information Systems, pages 1-19. 46

....points and a query point in an m dimensional metric space, find the data point that is closest to the query point. Particular interest has centered on solving this problem in high dimensional spaces, which arise from techniques that approximate (e.g. see [24] complex data such as images (e.g. [15, 27, 28, 21, 28, 23, 25, 18, 3]) sequences (e.g. 2, 1] video (e.g. 15] and shapes (e.g. This work was partially supported by a David and Lucile Packard Foundation Fellowship in Science and Engineering , a Presidential Young Investigator award, NASA research grant NAGW 3921, ORD contract 144 ET33, and NSF grant ....

U. M. Fayyad and P. Smyth. Automated analysis and exploration of image databases: Results, progress and challenges. Journal of intelligent information systems, 4(1):7--25, 1995.

....points and a query point in an mdimensional metric space, find the data point that is closest to the query point. Particular interest has centered on solving this problem in high dimensional spaces, which arise from techniques that approximate (e.g. see [22] complex data such as images (e.g. [13, 24, 25, 19, 25, 21, 23, 16, 3]) sequences (e.g. 2, 1] video (e.g. 13] and shapes (e.g. 13, 26, 23, 20] with long feature This work was partially supported by a David and Lucile Packard Foundation Fellowship in Science and Engineering , a Presidential Young Investigator award, NASA research grant NAGW 3921, ....

U. M. Fayyad and P. Smyth. Automated analysis and exploration of image databases: Results, progress and challenges. Journal of intelligent information systems, 4(1):7--25, 1995.

....manually. However, here the aim was to handle stars and nebulae considerably fainter than either visual inspection or existing computer methods could support, and attempts to handcraft expert systems for the task had not produced reliable advances. In response, Fayyad, Smyth, Weir, and Djorgovski [6]) adapted a machine learning approach to the problem. First they used image processing techniques to describe each object in a set of images in terms of standard numerical attributes, such as object magnitude, area, ellipticity, and statistical moments of object and core brightness. After ....

....an effective representation of the phenomena was central to most of the projects we examined. In some cases, this involved little more than talking with domain experts and getting their advice on attributes that were likely to have predictive value. In other cases (e.g. Fayyad et al. s work [6]) it involved a painstaking search of the feature space, looking for descriptors that could provide the discriminating power the more obvious features lacked. In some cases the primitive features may be computed by already established methods. Fayyad et al. relied heavily on established ....

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Fayyad, U. M., Smyth, P., Weir, N., & Djorgovski, S. (1995). Automated analysis and exploration of image databases: Results, progress, and challenges. Journal of Intelligent Information Systems, 4 , 1--19. Applications of Machine Learning 17

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Fayyad, Usama, Padhraic Smyth, N. Weir, and S. Djorgovski. 1995. Automated analysis and exploration of image databases: results, progress, and challenges. Journal of Intelligent Information Systems, 4:1--19.

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Fayyad, U. M., Smyth, P., Weir, N., & Djorgovski, S. (1995). Automated analysis and exploration of image databases: Results, progress, and challenges. Journal of Intelligent Information Systems, 4 , 1--19.

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