Can we build a feature-based analysis that fully characterizes images? The literature answers with edge-based reconstruction methods inspired by Marr's paradigm but limited to the greyscale case. This paper studies the color case. A new sparse representation is carried out with the monogenic concept and the Mallat-Zhong wavelet maxima method. Our monogenic maxima provide efficient contour shape and color characterization, as a sparse set of local features including amplitude, phase, orientation and ellipse parameters. This rich description takes the wavelet maxima representation further towards the wide topic of keypoint analysis. We propose a reconstruction process that retrieves the image from its monogenic maxima. While known works all rely on constrained optimization, implying an iterative use of the filterbank, we propose to interpolate the data in the feature domain by exploiting the visual knowledge from the feature-set. This direct retrieval is accurate enough so that no iteration is required. The main question is finally answered with comparative experiments. It is shown that a reasonably small amount of features is sufficiently informative for visually appealing image retrieval. The features appear numerically stable to rotation, and can be intuitively simplified to perform image regularization.

We define a new color extension for the monogenic representation of images by using an elliptical tri-valued oscillation model jointly with the vector structure tensor formalism. The proposed method provides a rich local colorimetric and geometric analysis, in particular a color phase concept, which can be computed by a numerically stable algorithm. This representation is finally used to estimate the local frequency of color images.

Monogenic wavelets offer a geometric representation of grayscale images through an AM-FM model allowing invariance of coefficients to translations and rotations. The underlying concept of local phase includes a fine contour analysis into a coherent unified framework. Starting from a link with structure tensors, we propose a nontrivial extension of the monogenic framework to vector-valued signals to carry out a nonmarginal color monogenic wavelet transform. We also give a practical study of this new wavelet transform in the contexts of sparse representations and invariant analysis, which helps to understand the physical interpretation of coefficients and validates the interest of our theoretical construction.

We propose a new color extension of the monogenic wavelet transform. Monogenic wavelets give a coherent representation of scalar images through a local phase concept and an underlying orientation analysis. We here define a color extension of the monogenic framework. The underlying local geometric analysis and phase concept are generalized by using the color structure tensor. Resulting transform appears to be a clear improvement of our previous work - that is the only proposition of color monogenic wavelets to our knowledge. This is shift and rotation invariant and efficiently represents multiscale color lines and edges.

We define a color monogenic wavelet transform. This is based on the recent grayscale monogenic wavelet transform and a non-marginal extension to color signals. To our knowledge, wavelet based color image processing schemes have always been made by using a grayscale tool separately on color channels. This may have some unexpected effect on colors because those marginal schemes are not necessarily justified. Here we propose a definition that considers a color (vector) image right at the beginning of the mathematical definition and so brings an actual color wavelet transform - which has not been done so far to our knowledge. This so provides a promising multiresolution color geometric analysis of images.

This paper proposes a new texture classifier based on the Quaternionic Wavelet Transform (QWT). This recent transform separates the informations contained in the image better than a classical wavelet transform (DWT), and provides a multiscale image analysis which coefficients are 2D analytic, with one near-shift invariant magnitude and a phase, that is made of three angles. The interpretation and use of the QWT coefficients, especially the phase, are discussed, and we present a texture classifier using both the QWT magnitude and the QWT phase of images. Our classifier performs a better recognition rate than a standard wavelet based classifier.