Combining global and local information for knowledge-assisted image analysis and classification
EURASIP Journal on Advances in Signal Processing
Anechoic Blind Source Separation Using Wigner Marginals
The Journal of Machine Learning Research
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Image retrieval methods aim to retrieve relevant images from an image database that are similar to the query image. The ability to effectively retrieve non-alphanumeric data is a complex issue. The problem becomes even more difficult due to the high dimension of the variable space associated with the images. Image classification is a very active and promising research domain in the area of image management and retrieval. In this paper, we propose a new image classification and retrieval scheme that automatically selects the discriminating features. Our method consists of two phases: (i) classification of images on the basis of maximum cross correlation and (ii) retrieval of images from the database against a given query image. The proposed retrieval algorithm recursively searches similar images on the basis of their correlation against a given query image from a set of registered images in the database. The algorithm is very efficient, provided that the mean images of all of the classes are computed and available in advance. The proposed method classifies the images on the basis of maximum correlation so that the images with more similarities and, hence, exhibiting maximum correlation with each other are grouped in the same class and, are retrieved accordingly.