We deal with the problem of time-efficient extraction of structural features in a large class of structural texture images. The proposed approach of multiscale morphological texture modeling describes explicitly and concisely both shape and intensity parameters in the structural texture model. The modeling is based on a morphological skeletal representation of structural texture cells as objects of interest and the genomic growth of a texture region starting from a seed cell. This representation offers the advantage of concise description of texture cells as compared to the existing edge-based or contour-based approaches. A computationally efficient estimation of the structural texture parameters for texture segmentation tasks is proposed. The model parameter estimation and subsequent feature extraction rely on cell localization and scale-based locally adaptive binarization of the localized cells using isotropic matched filtering. The multiscale isotropic matched filter (MIMF) provides a scale- and orientation-invariant detection of structural cells regarded as multiple objects of interest in texture regions. Results of experiments pertaining to the parameter estimation from synthetic and real texture images as well as the segmentation of texture regions based on structural features are also provided.
In this paper, we describe a three-step content-based approach to image retrieval and mining. At a first step, visual features such as color and shape are generated from images by improving a few existing feature extraction techniques. Then, both visual features and descriptive data (i.e., metadata) are considered for a coarse-grain similarity analysis using a conceptual clustering approach called formal concept analysis (concept lattice theory). This approach is designed and implemented so that exploratory mechanisms such as browsing, zooming/shrinking and visualization allow the user to discover and refine the cluster which is the most appropriate to his/her target image. At this second stage, issues such as dimension reduction, cluster construction and association rule generation are handled. The last step consists to conduct a fine-grain similarity analysis on some selected cluster(s) identified at the second stage by using two newly proposed similarity measures.