Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vector approximation based indexing for non-uniform high dimensional data sets
Proceedings of the ninth international conference on Information and knowledge management
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Efficient high-dimensional indexing by sorting principal component
Pattern Recognition Letters
Fast search in large-scale image database using vector quantization
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
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A practical method for creating a high dimensional index structure that adapts to the data distribution and scales well with the database size, is presented. Typical media descriptors are high dimensional and are not uniformly distributed in the feature space. The performance of many existing methods degrade if the data is not uniformly distributed. The proposed method offers an efficient solution to this problem. First, the data's marginal distribution along each dimension is characterized using a Gaussian mixture model. The parameters of this model are estimated using the well known Expectation-Maximization (EM) method. These model parameters can also be estimated sequentially for on-line updating. Using the marginal distribution information, each of the data dimensions can be partitioned such that each bin contains approximately an equal number of objects. Experimental results on a real image texture data set are presented. Comparisons with existing techniques, such as the VA-File, demonstrate a significant overall improvement.