Anastasios D. Doulamis

Equivalent Key Frames Selection Based on Iso-Content Principles

We present a key frames selection algorithm based on three iso-content principles (iso-content distance, iso-content error and iso-content distortion), so that the selected key frames are equidistant in video content according to the used principle. Two automatic approaches for defining the most appropriate number of key frames are proposed by exploiting supervised and unsupervised content criteria. Experimental results and the comparisons with existing methods from literature on large dataset of real-life video sequences illustrate the high performance of the proposed schemata.

Optimizing Resource Conflicts in Workflow Management Systems

Resource allocation and scheduling are fundamental issues in a Workflow Management System (WfMS). Effective resource management in WfMS should examine resource allocation together with task scheduling since these problems impose mutual constraints. Optimization of the one factor is subject to the other constraints and vice versa. Thus, an ideal algorithm should take into account not only performance metrics of the infrastructure, such as the number of resources and their utilization, but also quality criteria such as the percentage of tasks undergone violation in their temporal restrictions. In this paper, we propose an innovative algorithm which jointly optimizes the two aforementioned contradictory criteria. The algorithm, called Resource Conflicts Joint Optimization (Re.Co.Jo.Op.), minimizes resource conflicts subject to temporal constraints and simultaneously optimizes throughput or utilization subject to resources constraints. To achieve the optimization, the two factors are formulated in a matrix form and the optimal solution is found by applying concepts of the generalized eigenvalue analysis. A rough outline of an agent-based architecture is proposed to achieve runtime integration of our algorithm into a functional WfMS, while experimental results under different load environments and tasks assumption reveal the superiority of the proposed strategy than the other conventional approaches.

A System for Multicamera Task Recognition and Summarization for Structured Environments

In this paper, we propose a novel system for visual recognition and summarization of pick and place tasks that may be executed in settings such as an industrial assembly line. Our novel approach is based on the utilization of hidden Markov models for online task recognition as well as on the use of prior knowledge via a Hopfield-based optimization scheme. To facilitate offline analysis, we extract summaries of the captured content based on these features. We extract the motion energy using the norms of the Zernike moments, looking for local minima and maxima that indicate distinctive visual events and thus key-frames. The proposed scheme is not threshold-dependent, and, therefore, the number of extracted key-frames varies according to the complexity of motion energy variation. We validate our system by experimenting on two datasets.

Content-based Video Adaptation in Low/Variable Bandwidth Communication Networks Using Adaptable Neural Network Structures

In this paper, an adaptable neural network model is used for real time video delivery over communication networks of low and variable bandwidth, such as the wireless ones. The scheme performs video delivery in content domain in contrast to the previous approaches in which only temporal frame skipping is adopted. The proposed method requires no buffering of video frames and thus imposing no frame delay. In particular, in case of low bandwidth conditions, the proposed scheme estimates the number of frames that best represent the sequence within a time segment and transmit this number for delivery instead of a temporal frame skipping. Multiple key frames are considered by optimally approximating the real bandwidth availability with a rational fraction. Key frame estimation is accomplished using a neural network model capable of predicting the indices of the most appropriate key frames that are to be delivered without being available the video information. The model takes into account the previous information as it has been evaluated by the already delivered information. The proposed scheme is based on an efficient recursive estimation algorithm since the network weights cannot be considered constant throughout video transmission. This is due to the fact that content as well as bandwidth characteristics vary from time to time.

Vision-based maritime surveillance system using fused visual attention maps and online adaptable tracker

This paper presents a vision-based system for maritime surveillance using moving PTZ cameras. This system is intended to be used as an early warning system by local authorities. It fuses a visual attention method that exploits low-level image features appropriately selected for maritime environment, with an online adaptable neural network tracker, without making any assumptions about environmental or visual conditions. Systems performance was evaluated with videos from cameras placed at Limassol port and Venetian port of Chania and concerns robustness compared to dynamically changing visual conditions and different kinds of vessels, all in real time.

Equivalent Key Frames Selection Based on Iso-Content Distance and Iso-Distortion Principles

We present a key frames selection algorithm, which is very flexible on any changes of content descriptors, based on iso-content distance and iso-distortion principles. In both of the cases, the equality principle provides to the selected key frames the property to be equivalent on content video summarization. The estimated key frames properties and the experimental results indicate the good performance of the proposed schemata.

Transforming Intangible Folkloric Performing Arts into Tangible Choreographic Digital Objects: The Terpsichore Approach.

Intangible Cultural Heritage is a mainspring of cultural diversity and as such it should be a focal point in cultural heritage preservation and safeguarding endeavours. Nevertheless, although significant progress has been made in digitization technology as regards tangible cultural assets and especially in the area of 3D reconstruction, the e-documentation of intangible cultural heritage has not seen comparable progress. One of the main reasons associated lies in the significant challenges involved in the systematic e-digitisation of intangible cultural assets, such as performing arts. In this paper, we present at a high-level an approach for transforming intangible cultural assets, namely folk dances, into tangible choreographic digital objects. The approach is being implemented in the context of the H2020 European project “Terpsichore”.

An Automatic Multi-Agent Web Image and Associated Keywords Retrieval System

Web-based image search engines and CBIR techniques are blind to the actual content. As a result querying for a specific object is often cluttered with irrelevant data, leading to low precision. Furthermore recall rates are also very low since retrieval procedures are usually based either on context (surrounding text) and file captions or on low-level visual features. In this paper an automatic multi-agent image retrieval system is proposed. Our novel system exploits the format of multimedia sharing Web sites to discover the underlying structure in order to finally infer and extract multimedia files and corresponding associated keywords from the Web pages. The system first identifies the section of the Web page that contains the multimedia file to be extracted and then extracts it by using clustering techniques and other tools of statistical origin. Experimental results on real-world image sharing Web sites are presented and discussed in this paper, indicating the promising performance of the proposed system.

Tapped delay multiclass support vector machines for industrial workflow recognition

In this paper, a tapped delay multiclass support vector machine scheme is used for supervised job classification, based on video data taken from Nissan factory. The procedure is based on multiclass SVMs enhanced with the time dimension by incorporating additional information of n-th previous frames and allowing for user feedback when necessary. Such methodology will support the visual supervision of industrial environments by providing essential information to the supervisors and supporting their job.

Distributed Multimedia Metadata Tracking and Management: An Ontology-Based Approach with Use of RSS

In this paper we present a multimedia metadata management system that uses ontologies for efficient multimedia traceability and management. The use of digital devices to produce image and video content even by novices is expected to significantly increase the amount of multimedia data. This raises the need of adoption of an effective ontology-based model for intelligent multimedia metadata management. The problem assumes greater significance due to different file formats and the necessity of integrating largely distributed and diverse information system implementations. Provided that efficient searching and retrieval systems are based on how annotated data are organized and managed, in our study we use the RSS 2.0 (Really Simple Syndication) format, for enhancing the ontology with multimedia information.