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 [1]) for improving the correspondence of segmentation results produced by the well-known Recursive Shortest Spanning Tree (RSST) algorithm [2] 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 [3] 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

Interactive object contour extraction for shape modeling

In this paper we present a semi-automatic segmentation approach suitable for extracting object contours as a precursor to 2D shape modeling. The approach is a modified and extended version of an existing state-of-the-art approach based on the concept of a Binary Partition Tree (BPT) [1]. The resulting segmentation tool facilitates quick and easy extraction of an object’s contour via a small amount of user interaction that is easy to perform, even in complicated scenes. Illustrative segmentation results are presented and the usefulness of the approach in generating object shape models is discussed

Region-based segmentation of images using syntactic visual features

This paper presents a robust and efficient method for segmentation of images into large regions that reflect the real world objects present in the scene. We propose an extension to the well known Recursive Shortest Spanning Tree (RSST) algorithm based on a new color model and so-called syntactic features [1]. We introduce practical solutions, integrated within the RSST framework, to structure analysis based on the shape and spatial configuration of image regions. We demonstrate that syntactic features provide a reliable basis for region merging criteria which prevent formation of regions spanning more than one semantic object, thereby significantly improving the perceptual quality of the output segmentation. Experiments indicate that the proposed features are generic in nature and allow satisfactory segmentation of real world images from various sources without adjustment to algorithm parameters

Image segmentation evaluation using an integrated framework

In this paper we present a general framework we have developed for running and evaluating automatic image and video segmentation algorithms. This framework was designed to allow effortless integration of existing and forthcoming image segmentation algorithms, and allows researchers to focus more on the development and evaluation of segmentation methods, relying on the framework for encoding/decoding and visualization. We then utilize this framework to automatically evaluate four distinct segmentation algorithms, and present and discuss the results and statistical findings of the experiment

A framework and user interface for automatic region based segmentation algorithms

In this paper we describe a framework and tool developed for running and evaluating automatic region based segmentation algorithms. The tool was designed to allow simple integration of existing and future segmentation algorithms, both single image based algorithms and those that operate on video data. Our framework supports plug-in segmenters, media decoders, and region-map codecs. We provide several sophisticated implementations of these plug-ins, including a video decoder capable of frame accurate decoding of a large variety of video formats, an image decoder which also handles a comprehensive collection of formats, and a efficient implementation of a region-map codec. The tool includes both a graphical user interface to allow users to browse, visually inspect, and evaluate the algorithm output, and a batch processing interface for segmentation of large data collections. The application allows researchers to focus more on the development and evaluation of segmentation methods, relying on the framework for encoding/decoding input and output, and the front end for visualization

Multi-view 3D retrieval using silhouette intersection and multi-scale contour representation

We describe in this paper two methods for 3D shape indexing and retrieval that we apply on two data collections of the SHREC - SHape Retrieval Contest 2007: Watertight models and 3D CAD models. Both methods are based on a set of 2D multi-views after a pose and scale normalization of the models using PCA and the enclosing sphere. In all views we extract the models silhouettes and compare them pairwise. In the first method the similitude measure is obtained by integrating on the pairs of views the difference between the areas of the silhouettes union and the silhouettes intersection. In the second method we consider the external contour of the silhouettes, extract their convexities and concavities at different scale levels and build a multiscale representation. The pairs of contours are then compared by elastic matching achieved by using dynamic programming. Comparisons of the two methods are shown with their respective strengths and weaknesses

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

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

TRECVid 2006 experiments at Dublin City University

In this paper we describe our retrieval system and experiments performed for the automatic search task in TRECVid 2006. We submitted the following six automatic runs: • F A 1 DCU-Base 6: Baseline run using only ASR/MT text features. • F A 2 DCU-TextVisual 2: Run using text and visual features. • F A 2 DCU-TextVisMotion 5: Run using text, visual, and motion features. • F B 2 DCU-Visual-LSCOM 3: Text and visual features combined with concept detectors. • F B 2 DCU-LSCOM-Filters 4: Text, visual, and motion features with concept detectors. • F B 2 DCU-LSCOM-2 1: Text, visual, motion, and concept detectors with negative concepts. The experiments were designed both to study the addition of motion features and separately constructed models for semantic concepts, to runs using only textual and visual features, as well as to establish a baseline for the manually-assisted search runs performed within the collaborative K-Space project and described in the corresponding TRECVid 2006 notebook paper. The results of the experiments indicate that the performance of automatic search can be improved with suitable concept models. This, however, is very topic-dependent and the questions of when to include such models and which concept models should be included, remain unanswered. Secondly, using motion features did not lead to performance improvement in our experiments. Finally, it was observed that our text features, despite displaying a rather poor performance overall, may still be useful even for generic search topics

The aceToolbox: low-level audiovisual feature extraction for retrieval and classification

In this paper we present an overview of a software platform that has been developed within the aceMedia project, termed the aceToolbox, that provides global and local lowlevel feature extraction from audio-visual content. The toolbox is based on the MPEG-7 eXperimental Model (XM), with extensions to provide descriptor extraction from arbitrarily shaped image segments, thereby supporting local descriptors reflecting real image content. We describe the architecture of the toolbox as well as providing an overview of the descriptors supported to date. We also briefly describe the segmentation algorithm provided. We then demonstrate the usefulness of the toolbox in the context of two different content processing scenarios: similarity-based retrieval in large collections and scene-level classification of still images