Marcin Grzegorzek

Semantic Multimedia

Multimedia constitutes an interesting field of application for Semantic Web and Semantic Web reasoning, as the access and management of multimedia content and context depends strongly on the semantic descriptions of both. At the same time, multimedia resources constitute complex objects, the descriptions of which are involved and require the foundation on sound modeling practice in order to represent findings of low- and high level multimedia analysis and to make them accessible via Semantic Web querying of resources. This tutorial aims to provide a red thread through these different issues and to give an outline of where Semantic Web modeling and reasoning needs to further contribute to the area of semantic multimedia for the fruitful interaction between these two fields of computer science.

Multi-scale textural feature extraction and particle swarm optimization based model selection for false positive reduction in mammography

The high number of false positives and the resulting number of avoidable breast biopsies are the major problems faced by current mammography Computer Aided Detection (CAD) systems. False positive reduction is not only a requirement for mass but also for calcification CAD systems which are currently deployed for clinical use. This paper tackles two problems related to reducing the number of false positives in the detection of all lesions and masses, respectively. Firstly, textural patterns of breast tissue have been analyzed using several multi-scale textural descriptors based on wavelet and gray level co-occurrence matrix. The second problem addressed in this paper is the parameter selection and performance optimization. For this, we adopt a model selection procedure based on Particle Swarm Optimization (PSO) for selecting the most discriminative textural features and for strengthening the generalization capacity of the supervised learning stage based on a Support Vector Machine (SVM) classifier. For evaluating the proposed methods, two sets of suspicious mammogram regions have been used. The first one, obtained from Digital Database for Screening Mammography (DDSM), contains 1494 regions (1000 normal and 494 abnormal samples). The second set of suspicious regions was obtained from database of Mammographic Image Analysis Society (mini-MIAS) and contains 315 (207 normal and 108 abnormal) samples. Results from both datasets demonstrate the efficiency of using PSO based model selection for optimizing both classifier hyper-parameters and parameters, respectively. Furthermore, the obtained results indicate the promising performance of the proposed textural features and more specifically, those based on co-occurrence matrix of wavelet image representation technique.

Comparative evaluation of spatial context techniques for semantic image analysis

In this paper, two approaches to utilizing contextual information in semantic image analysis are presented and comparatively evaluated. Both approaches make use of spatial context in the form of fuzzy directional relations. The first one is based on a Genetic Algorithm (GA), which is employed in order to decide upon the optimal semantic image interpretation by treating semantic image analysis as a global optimization problem. On the other hand, the second method follows a Binary Integer Programming (BIP) technique for estimating the optimal solution. Both spatial context techniques are evaluated with several different combinations of classifiers and low-level features, in order to demonstrate the improvements attained using spatial context in a number of different image analysis schemes.

Classification of environmental microorganisms in microscopic images using shape features and support vector machines

Environmental Microorganisms (EMs) are currently recognised using molecular biology (DNA, RNA) or morphological methods. The first ones are very time-consuming and expensive. The second ones require a very experienced laboratory operator. To overcome these problems, we introduce an automatic classification method for EMs in the framework of content-based image analysis in this paper. To describe the shapes of EMs observed in microscopic images, we use Edge Histograms, Fourier Descriptors, extended Geometrical Features, as well as introduce Internal Structure Histograms. For classification, multi-class Support Vector Machine is applied to EMs represented by the above features. In order to quantitatively evaluate discriminative properties of the feature spaces we have introduced, we perform comprehensive experiments with a ground truth of manually segmented microscopic EM images. The best classification result of 89.7% proves a high robustness of our method in this application domain.

Object matching with hierarchical skeletons

The skeleton of an object provides an intuitive and effective abstraction which facilitates object matching and recognition. However, without any human interaction, traditional skeleton-based descriptors and matching algorithms are not stable for deformable objects. Specifically, some fine-grained topological and geometrical features would be discarded if the skeleton was incomplete or only represented significant visual parts of an object. Moreover, the performance of skeleton-based matching highly depends on the quality and completeness of skeletons. In this paper, we propose a novel object representation and matching algorithm based on hierarchical skeletons which capture the shape topology and geometry through multiple levels of skeletons. For object representation, we reuse the pruned skeleton branches to represent the coarse- and fine-grained shape topological and geometrical features. Moreover, this can improve the stability of skeleton pruning without human interaction. We also propose an object matching method which considers both global shape properties and fine-grained deformations by defining singleton and pairwise potentials for similarity computation between hierarchical skeletons. Our experiments attest our hierarchical skeleton-based method a significantly better performance than most existing shape-based object matching methods on six datasets, achieving a 99.21% bulls-eye score on the MPEG7 shape dataset. HighlightsIt represents the coarse- and fine-grained shape topological and geometrical features.It improves the stability of skeleton pruning without human interaction.It considers both global and fine shape properties by different potential functions.It achieves a better performance than most existing methods on six datasets.Experiments attest our method a better performance than most related approaches.We achieve a 99.21% bulls-eye score on the MPEG7 shape dataset.

Shape-based object retrieval by contour segment matching

In this paper we introduce an approach for object retrieval that uses contour segment matching for shape similarity computation. The object contour is partitioned into segments by skeleton endpoints. Each contour segment is represented by a rotation and scale invariant, 12-dimensional feature vector. The similarity of two objects is determined by matching their contour segments using the Hungarian algorithm. Our method is insensitive to object deformation and outperforms existing shape-based object retrieval algorithms. The most significant scientific contributions of this paper include (i) the introduction of a new feature extraction technique for contour segments as well as (ii) a new similarity measure for contour segments cleverly modelling the human perception and easily adapting to concrete application domains, and (iii) the impressive robustness of the method in an object retrieval scenario.

Appearance-based recognition of 3-D objects by cluttered background and occlusions

In this article we present a new appearance-based approach for the classification and the localization of 3-D objects in complex scenes. A main problem for object recognition is that the size and the appearance of the objects in the image vary for 3-D transformations. For this reason, we model the region of the object in the image as well as the object features themselves as functions of these transformations. We integrate the model into a statistical framework, and so we can deal with noise and illumination changes. To handle heterogeneous background and occlusions, we introduce a background model and an assignment function. Thus, the object recognition system becomes robust, and a reliable distinction, which features belong to the object and which to the background, is possible. Experiments on three large data sets that contain rotations orthogonal to the image plane and scaling with together more than 100000 images show that the approach is well suited for this task.

Multi sensor 3D indoor localisation

We present an indoor localisation system that integrates different sensor modalities, namely Wi-Fi, barometer, iBeacons, step-detection and turn-detection for localisation of pedestrians within buildings over multiple floors. To model the pedestrians movement, which is constrained by walls and other obstacles, we propose a state transition based upon random walks on graphs. This model also frees us from the burden of frequently updating the system. In addition we make use of barometer information to estimate the current floor. Furthermore, we present a statistical approach to avoid the incorporation of faulty heading information caused by changing the smartphones position. The evaluation of the system within a 77m × 55m sized building with 4 floors shows that high accuracy can be achieved while also keeping the update-rates low.

Shape-Based Classification of Environmental Microorganisms

Occurrence of certain environmental microorganisms and their species is a very informative indicator to evaluate environmental quality. Unfortunately, their manual recognition in microbiological laboratories is very time-consuming and expensive. Therefore, we work on an automatic method for shape-based classification of EMs in microscopic images. First, we segment the microorganisms from the background. Second, we describe their shapes by discriminative feature vectors. Third, we perform the EM classification using Support Vector Machines. The most important scientific contribution of this paper, in comparison to the state-of-the-art and to our previous publications in this field, is the introduction of a completely new and very robust 2D feature descriptor for EM shapes. Experimental results certify the effectiveness and practicability of our automatic EM classification system emphasising the benefits achieved with the new shape descriptor proposed in this work.

Local Wavelet Features for Statistical Object Classification and Localization

This article presents a system for texture based probabilistic classification and localization of 3D objects in 2D digital images and discusses selected applications.