Elena Galán-Perales

Retrieving images in fuzzy object-relational databases using dominant color descriptors

In this paper a fuzzy approach for image retrieval on the basis of color features is presented. The proposal deals with vagueness in the color description and introduces the use of fuzzy database models to store and retrieve imprecise data. To face the color description, the concept of dominant fuzzy color is proposed, using linguistic labels for representing the color information in terms of hue, saturation and intensity. To deal with fuzzy data in our database model, we use a general approach which can support the manipulation of fuzzy objects in an object-relational database system. This allows the retrieval of images by performing flexible queries on the database.

A hierarchical approach to fuzzy segmentation of colour images

In this paper we introduce a methodology for the segmentation of colour images by means of a nested hierarchy of fuzzy partitions. Colour image segmentation attempts to divide the pixels of an image in several homogeneously-coloured and topologically connected groups, called regions. Our methodology deals with the different (but related) aspects of imprecision that are present in this process. First, the concept of homogeneity in a colour space is imprecise, so a measure of distance/similarity between colours is introduced. As a direct consequence, boundaries between regions are imprecise in general, so it is convenient to define regions as fuzzy subsets of items. The proposed distance in a perceptual colour space is employed to calculate fuzzy regions and membership degrees. In addition, fuzzy segmentation can be different depending on the precision level we consider when looking for homogeneity. Starting from an initial fuzzy segmentation, a hierarchical approach, based on a similarity relation between regions, is employed to obtain a nested hierarchy of regions at different precision levels.

Using Fuzzy Sets for Coarseness Representation in Texture Images

Texture is a visual feature frequently used in image analysis that has associated certain vagueness. However, the majority of the approaches found in the literature do not either consider such vagueness or they do not take into account human perception to model the related uncertainty. In this paper we model the concept of ”coarseness”, one of the most important textural features, by means of fuzzy sets and considering the way humans perceive this kind of texture. Specifically, we relate representative measures of coarseness with its presence degree. To obtain these ”presence degrees”, we collect assessments from polls filled by human subjects, performing an aggregation of such assessments. Thus, the membership function corresponding to the fuzzy set ”coarseness” is modelled by using as reference set the representative measures and the aggregated data.

Fuzzy Homogeneity Measures for Path-Based Colour Image Segmentation

In this paper we study different measures of path homogeneity for fuzzy path-based image segmentation. We provide fuzzy semantics for the concept of homogeneity in two steps: first, we introduce a fuzzy interpretation of resemblance between feature vectors characterizing neighbor pixels; then, we obtain the homogeneity of a path by aggregating the set of fuzzy resemblances between consecutive pixels in the path. We propose a set of intuitive properties that any suitable aggregation function should verify for this purpose, and we show that these properties are verified by certain families of t-norms. To determine the performance of the proposed functions, a set of experiments is carried out with both synthetic and real images. Finally, the homogeneity functions are used to obtain fuzzy regions in natural images

Fuzzy Sets for Modelling Fineness Perception in Texture Images

Fineness is a primary texture feature frequently used for image content description. However, it is an ambiguous concept difficult to be characterized. To face this ambiguity, we propose to model the fineness by means of fuzzy sets, relating a representative fineness measure (our reference set) with the human perception of fineness. In our study, a wide variety of measures have been analyzed, defining a fuzzy set for each measure. The fineness perception has been collected from polls filled by human subjects, performing an aggregation of their assessments by means of OWA operators. For a given measure, the corresponding membership function is obtained by fitting the collected data. The performance of each fuzzy set is analyzed and checked with the human assessments, proposing a subgroup of them as the most adequate for modelling fineness perception in texture images.

Perceptually-Based Functions for Coarseness Textural Feature Representation

Coarseness is a very important textural concept that has been widely analyzed in computer vision for years. However, a model which allows to represent different perception degrees of this textural concept in the same way that humans perceive texture is needed. In this paper we propose a model that associates computational measures to human perception by learning an appropriate function. To do it, different measures representative of coarseness are chosen and subjects assessments are collected and aggregated. Finally, a function that relates these data is fitted.

Fuzzy homogeneity measures for path-based colour image segmentation

In this paper we propose a set of measures to model the concept of homogeneity in path-based image segmentation. We introduce the idea of path homogeneity as the aggregation of resemblances between consecutive pixels in the path. This resemblance is obtained from a measure of resemblance between neighbour pixels. In order to aggregate these resemblance values we propose the use of certain families of t-norms that verify a set of intuitive properties. We have studied the performance and behaviour of these functions through a set of experiments. Finally, we have applied these proposals to obtain fuzzy segmentations from real images.

Modelling coarseness in texture images by means of fuzzy sets

In this paper we model the concept of coarseness, typically used in texture image descriptions, by means of fuzzy sets. Specifically, we relate representative measures of this kind of texture with its presence degree. To obtain these presence degrees, we collect assessments from polls filled by human subjects, performing an aggregation of these assessments by means of OWA operators. Using this collected data, and some statistics as reference set, the membership function corresponding to the fuzzy set coarseness is modelled.

Dealing with multiple motions in optical flow estimation

In this paper, a new approach to optical flow estimation in presence of multiple motions is presented. Firstly, motions are segmented on the basis of a frequency-based approach that groups spatio-temporal filter responses with continuity in its motion (each group will define a motion pattern). Then, the gradient constraint is applied to the output of each filter so that multiple estimations of the velocity at the same location may be obtained. For each “motion pattern”, the velocities at a given point are then combined using a probabilistic approach. The use of “motion patterns” allows multiple velocities to be represented, while the combination of estimations from different filters helps reduce the aperture problem.