title: Fast wavelet-based image characterization for highly adaptive image retrieval.
abstract: Adaptive wavelet-based image characterizations have been proposed in previous works for content-based image retrieval (CBIR) applications. In these applications, the same wavelet basis was used to characterize each query image: This wavelet basis was tuned to maximize the retrieval performance in a training data set. We take it one step further in this paper: A different wavelet basis is used to characterize each query image. A regression function, which is tuned to maximize the retrieval performance in the training data set, is used to estimate the best wavelet filter, i.e., in terms of expected retrieval performance, for each query image. A simple image characterization, which is based on the standardized moments of the wavelet coefficient distributions, is presented. An algorithm is proposed to compute this image characterization almost instantly for every possible separable or nonseparable wavelet filter. Therefore, using a different wavelet basis for each query image does not considerably increase computation times. On the other hand, significant retrieval performance increases were obtained in a medical image data set, a texture data set, a face recognition data set, and an object picture data set. This additional flexibility in wavelet adaptation paves the way to relevance feedback on image characterization itself and not simply on the way image characterizations are combined.
authors.affiliation: LaTIM Inserm Research Unit 1101, 29200 Brest, France. gwenole.quellec@inserm.fr
authors.forename: Gwénolé
authors.initials: G
authors.lastname: Quellec
authors.forename: Mathieu
authors.initials: M
authors.lastname: Lamard
authors.forename: Guy
authors.initials: G
authors.lastname: Cazuguel
authors.forename: Béatrice
authors.initials: B
authors.lastname: Cochener
authors.forename: Christian
authors.initials: C
authors.lastname: Roux
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011873:Radiology Information Systems; D013382:Subtraction Technique; D058067:Wavelet Analysis