Structuring of large and heterogeneous texture databases

Abstract

Processing large databases is intricate for databases involving several types of textures. In particular, for content-based image retrieval, a query has to be compared with all the samples pertaining to the database in order to identify its content/class and this is time consuming. Furthermore, modeling of a large database is a difficult task for databases involving several types of textures since accurate models for certain textures are not guaranteed to be very relevant for other types of textures and vice-versa. In order to save computational time and increase performance in processing of large texture databases, the present paper proposes structuring texture databases by using stochasticity metaclasses.

Multidate Divergence Matrices for the Analysis of SAR Image Time Series

“…Among the variants of wavelet frames, we consider the SWT because of its suitable statistical properties [11], [16], [17] and the FDCT for its sensitivity to image geometry: the transform is a quasi-optimal representation for 2 di- mensional regular signals admitting some singularities like edges and contours for natural images [12], [13], [14]. The J-level SWT and FDCT representations of an image I have the following form:…”