6533b7d0fe1ef96bd125b61c

RESEARCH PRODUCT

A New Wavelet-Based Texture Descriptor for Image Retrieval

Daniel AcevedoAna M. C. RuedinEsther De VesXaro BenaventLeticia M. Seijas

subject

Local binary patternsbusiness.industryTexture DescriptorFeature extractionComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONWavelet transformPattern recognitionComputingMethodologies_PATTERNRECOGNITIONWaveletImage textureComputer Science::Computer Vision and Pattern RecognitionHistogramArtificial intelligencebusinessImage retrievalMathematics

description

This paper presents a novel texture descriptor based on the wavelet transform. First, we will consider vertical and horizontal coefficients at the same position as the components of a bivariate random vector. The magnitud and angle of these vectors are computed and its histograms are analyzed. This empirical magnitud histogram is modelled by using a gamma distribution (pdf). As a result, the feature extraction step consists of estimating the gamma parameters using the maxima likelihood estimator and computing the circular histograms of angles. The similarity measurement step is done by means of the well-known Kullback-Leibler divergence. Finally, retrieval experiments are done using the Brodatz texture collection obtaining a good performance of this new texture descriptor. We compare two wavelet transforms, with and without downsampling, and show the advantage of the second one, which is translation invariant, for the construction of our texture descriptor.

yearjournalcountryeditionlanguage
2007-01-01
https://doi.org/10.1007/978-3-540-74272-2_111