Multiresolution Wavelet Transform and Supervised Learning for Content-Based Image Retrieval

Authors:
C. Brambilla;A. Della Ventura;I. Gagliardi;R. Schettini
Affiliations:
Consiglio Nazionale delle Ricerche;Consiglio Nazionale delle Ricerche;Consiglio Nazionale delle Ricerche;Consiglio Nazionale delle Ricerche
Venue:
ICMCS '99 Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2
Year:
1999

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Abstract

We focus here on the definition of an effective strategy that allows the user to pose a visual query and retrieve a set of images from a database that satisfy his criteria of pictorial similarity without requiring any semantic expression of them. The strategy exploits multi-resolution wavelet transform to effectively describe image content. The salient features of the images are coded in signatures of predefined lengths which are compared in the retrieval phase by applying a similarity measure the system has pre-learned, using a regression model for ordinal responses, from a learning set of "very similar", "rather-similar", "not-very-similar", and "different" pairs of images. Some experimental results demonstrating the effectiveness of this approach are reported.