| Abstract |
During the last decades, digital information is being gathered and stored at an explosive rate on large digital databases. In the particular case of databases containing digital images, this fact gives rise to the important issue of effectively and precisely searching and interacting with these collections. Recently, it has become evident that commonly used deterministic retrieval techniques are often inadequate in describing the image content. However, there is a strong motivation to search for more powerful statistical retrieval methods that can capture the intrinsic structure of digital images, represented by low level features such as texture. Recent studies verified that in most cases these statistics are highly non-Gaussian, thus the previously developed retrieval methods based on a Gaussian assumption are unable to capture the true statistical behavior of the image features. This thesis introduces the family of Alpha-Stable distributions and stochastic processes for designing a texture-based image retrieval system. The performance of a retrieval system is improved by implementing a multi-scale and multi-orientation image transformation, resulting in a simpler and more efficient statistical model. In this framework, we build a hierarchical retrieval system, starting from a univariate model describing the marginal statistics at each orientation, and then by exploiting possible inter-dependencies between the transform coefficients at adjacent orientations and scales. We also enhance the capacity of the multivariate model in order to achieve rotation invariance. The efficiency of a retrieval system does not only depend on the set of features representing the texture information, but also on a suitable similarity function that measures how close to a given query each image in the database is. For each one of the proposed methods we construct appropriate similarity functions exploiting the accuracy of the Alpha-Stable model in capturing the marginal and joint non-Gaussian behavior of the transform coefficients. We illustrate the improved performance of our models compared with the performance of recently developed statistical retrieval schemes, by applying them on real texture samples.
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Ανάκτηση Εικόνας με βάση το Περιεχόμενο, μέσω Aλφα-Ευσταθούς Μοντεολοποίησης της Πληροφορίας Υφής
