Tomasz Adamek

Using Dempster-Shafer Theory to Fuse Multiple Information Sources in Region-Based Segmentation

This paper presents a new method for segmentation of images into large regions that reflect the real world objects present in a scene. It explores the feasibility of utilizing spatial configuration of regions and their geometric properties (the so-called syntactic visual features by C. Ferran Bennstrom and JR Casas (2004)) for improving the correspondence of segmentation results produced by the well-known recursive shortest spanning tree (RSST) algorithm by O.J. Morris et al. (1986) to semantic objects present in the scene. The main contribution of this paper is a novel framework for integration of evidence from multiple sources with the region merging process based on the Dempster-Shafer (DS) theory by P. Smets (1988) that allows integration of sources providing evidence with different accuracy and reliability. Extensive experiments indicate that the proposed solution limits formation of regions spanning more than one semantic object.

QIMERA: A SOFTWARE PLATFORM FOR VIDEO OBJECT SEGMENTATION AND TRACKING

In this paper we present an overview of an ongoing collaborative project in the field of video object segmentation and tracking. The objective of the project is to develop a flexible modular software architecture that can be used as test-bed for segmentation algorithms. The background to the project is described, as is the first version of the software system itself. Some sample results for the first segmentation algorithm developed using the system are presented and directions for future work are discussed.

Towards Fully Automatic Image Segmentation Evaluation

Spatial region (image) segmentation is a fundamental step for many computer vision applications. Although many methods have been proposed, less work has been done in developing suitable evaluation methodologies for comparing different approaches. The main problem of general purpose segmentation evaluation is the dilemma between objectivity and generality. Recently, figure ground segmentation evaluation has been proposed to solve this problem by defining an unambiguous ground truth using the most salient foreground object. Although the annotation of a single foreground object is less complex than the annotation of all regions within an image, it is still quite time consuming, especially for videos. A novel framework incorporating background subtraction for automatic ground truth generation and different foreground evaluation measures is proposed, that allows to effectively and efficiently evaluate the performance of image segmentation approaches. The experiments show that the objective measures are comparable to the subjective assessment and that there is only a slight difference between manually annotated and automatically generated ground truth.

SHREC’08 entry: Multi-view 3D retrieval using multi-scale contour representation

We describe in this paper a method for 3D shape indexing and retrieval that we apply on three data collections of the SHREC - SHape Retrieval Contest 2008: Stability on watertight, CAD and Generic 3D models. The method is based on a set of 2D multi-views after a pose and scale normalization of the models using Continuous PCA and the enclosing sphere. In all views we extract the models silhouettes and compare them pairwise. To compute the similitude measure we consider the external contour of the silhouettes, we extract their convexities and concavities at different scale levels and we build a multiscale representation. The pairs of contours are then compared by elastic matching achieved by using dynamic programming.

Stopping region-based image segmentation at meaningful partitions

This paper proposes a new stopping criterion for automatic image segmentation based on region merging. The criterion is dependent on image content itself and when combined with the recently proposed approaches to syntactic segmentation can produce results aligned with the most salient semantic regions/objects present in the scene across heterogeneous image collections. The method identifies a single iteration from the merging process as the stopping point, based on the evolution of an accumulated merging cost during the complete merging process. The approach is compared to three commonly used stopping criteria: (i) required number of regions, (ii) value of the least link cost, and (iii) Peak Signal to Noise Ratio (PSNR). For comparison, the stopping criterion is also evaluated for a segmentation approach that does not use syntactic extensions. All experiments use a manually generated segmentation ground truth and spatial accuracy measures. Results show that the proposed stopping criterion improves segmentation performance towards reflecting real-world scene content when integrated into a syntactic segmentation framework.

Inexpensive fusion methods for enhancing feature detection

Recent successful approaches to high-level feature detection in image and video data have treated the problem as a pattern classification task. These typically leverage the techniques learned from statistical machine learning, coupled with ensemble architectures that create multiple feature detection models. Once created, co-occurrence between learned features can be captured to further boost performance. At multiple stages throughout these frameworks, various pieces of evidence can be fused together in order to boost performance. These approaches whilst very successful are computationally expensive, and depending on the task, require the use of significant computational resources. In this paper we propose two fusion methods that aim to combine the output of an initial basic statistical machine learning approach with a lower-quality information source, in order to gain diversity in the classified results whilst requiring only modest computing resources. Our approaches, validated experimentally on TRECVid data, are designed to be complementary to existing frameworks and can be regarded as possible replacements for the more computationally expensive combination strategies used elsewhere.

Inexpensive Fusion Methods for Enhancing Feature Detection

In this paper we present two fusion methods for the task of high-level feature detection in multimedia content. Successful approaches to high-level feature detection typically leverage the techniques learned from Machine Learning utilized through ensemble architectures to achieve strong performance. However these approaches whilst successful are computationally expensive, and depending on the task require the use of significant computational resources. We propose two fusion methods that aim to combine the output of an initial basic machine learning approach with a lower-quality information source in order to gain diversity in the classified results whilst only requiring modest computing resources.

Incorporating spatio-temporal mid-level features in a region segmentation algorithm for video sequences

Segmentation algorithms traditionally employ low-level features to divide images into different regions that show a certain degree of homogeneity. However, low-level features, spatial or temporal, are not always reliable when processing real-world video sequences, because of issues like illuminations or complex backgrounds. Furthermore, real world objects can be composed of different regions with heterogeneous features. Although the inclusion of motion can mitigate some of these effects, many problems are still present. This paper proposes the utilization of some spatio-temporal mid-level features that are related, on the one hand, to geometric properties of real objects and, on the other, to well-known motion patterns. Specifically, the proposed algorithm uses a mid-level module that controls the subsequent segmentation using these kinds of features. Some experiments and evaluations show that the inclusion of mid-level features can help to obtain perceptually more meaningful segmentations, thus resulting in regions that are closer to semantic concepts.