Real-time video annotation using MPEG-7 motion activity descriptors

The MPEG-7 standard provides a framework of standardized tools that can be used to describe and efficiently manage multimedia content. Visual descriptors include color, texture, shape and motion. In this paper, we address the hardware implementation of MPEG-7 motion descriptors using Handel-C. In particular, descriptors for motion intensity and spatial distribution of motion activity are generated and implemented

Combining textual and visual information processing for interactive video retrieval: SCHEMA's participation in TRECVID 2004

In this paper, the two different applications based on the Schema Reference System that were developed by the SCHEMA NoE for participation to the search task of TRECVID 2004 are illustrated. The first application, named ”Schema-Text”, is an interactive retrieval application that employs only textual information while the second one, named ”Schema-XM”, is an extension of the former, employing algorithms and methods for combining textual, visual and higher level information. Two runs for each application were submitted, I A 2 SCHEMA-Text 3, I A 2 SCHEMA-Text 4 for Schema-Text and I A 2 SCHEMA-XM 1, I A 2 SCHEMA-XM 2 for Schema-XM. The comparison of these two applications in terms of retrieval efficiency revealed that the combination of information from different data sources can provide higher efficiency for retrieval systems. Experimental testing additionally revealed that initially performing a text-based query and subsequently proceeding with visual similarity search using one of the returned relevant keyframes as an example image is a good scheme for combining visual and textual information

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

The DICEMAN description schemes for still images and video sequences

To address the problem of visual content description, two Description Schemes (DSs) developed within the context of a European ACTS project known as DICEMAN, are presented. The DSs, designed based on an analogy with well-known tools for document description, describe both the structure and semantics of still images and video sequences. The overall structure of both DSs including the various sub-DSs and descriptors (Ds) of which they are composed is described. In each case, the hierarchical sub-DS for describing structure can be constructed using automatic (or semi-automatic) image/video analysis tools. The hierarchical sub-DSs for describing the semantics, however, are constructed by a user. The integration of the two DSs into a video indexing application currently under development in DICEMAN is also briefly described.Peer ReviewedPostprint (published version

DC-image for real time compressed video matching

This chapter presents a suggested framework for video matching based on local features extracted from the DC-image of MPEG compressed videos, without full decompression. In addition, the relevant arguments and supporting evidences are discussed. Several local feature detectors will be examined to select the best for matching using the DC-image. Two experiments are carried to support the above. The first is comparing between the DC-image and I-frame, in terms of matching performance and computation complexity. The second experiment compares between using local features and global features regarding compressed video matching with respect to the DC-image. The results confirmed that the use of DC-image, despite its highly reduced size, it is promising as it produces higher matching precision, compared to the full I-frame. Also, SIFT, as a local feature, outperforms most of the standard global features. On the other hand, its computation complexity is relatively higher, but it is still within the real-time margin which leaves a space for further optimizations that can be done to improve this computation complexity

K-Space at TRECVid 2007

In this paper we describe K-Space participation in TRECVid 2007. K-Space participated in two tasks, high-level feature extraction and interactive search. We present our approaches for each of these activities and provide a brief analysis of our results. Our high-level feature submission utilized multi-modal low-level features which included visual, audio and temporal elements. Specific concept detectors (such as Face detectors) developed by K-Space partners were also used. We experimented with different machine learning approaches including logistic regression and support vector machines (SVM). Finally we also experimented with both early and late fusion for feature combination. This year we also participated in interactive search, submitting 6 runs. We developed two interfaces which both utilized the same retrieval functionality. Our objective was to measure the effect of context, which was supported to different degrees in each interface, on user performance. The first of the two systems was a ‘shot’ based interface, where the results from a query were presented as a ranked list of shots. The second interface was ‘broadcast’ based, where results were presented as a ranked list of broadcasts. Both systems made use of the outputs of our high-level feature submission as well as low-level visual features

Techniques for effective and efficient fire detection from social media images

Social media could provide valuable information to support decision making in crisis management, such as in accidents, explosions and fires. However, much of the data from social media are images, which are uploaded in a rate that makes it impossible for human beings to analyze them. Despite the many works on image analysis, there are no fire detection studies on social media. To fill this gap, we propose the use and evaluation of a broad set of content-based image retrieval and classification techniques for fire detection. Our main contributions are: (i) the development of the Fast-Fire Detection method (FFDnR), which combines feature extractor and evaluation functions to support instance-based learning, (ii) the construction of an annotated set of images with ground-truth depicting fire occurrences -- the FlickrFire dataset, and (iii) the evaluation of 36 efficient image descriptors for fire detection. Using real data from Flickr, our results showed that FFDnR was able to achieve a precision for fire detection comparable to that of human annotators. Therefore, our work shall provide a solid basis for further developments on monitoring images from social media.Comment: 12 pages, Proceedings of the International Conference on Enterprise Information Systems. Specifically: Marcos Bedo, Gustavo Blanco, Willian Oliveira, Mirela Cazzolato, Alceu Costa, Jose Rodrigues, Agma Traina, Caetano Traina, 2015, Techniques for effective and efficient fire detection from social media images, ICEIS, 34-4

Advanced content-based semantic scene analysis and information retrieval: the SCHEMA project

The aim of the SCHEMA Network of Excellence is to bring together a critical mass of universities, research centers, industrial partners and end users, in order to design a reference system for content-based semantic scene analysis, interpretation and understanding. Relevant research areas include: content-based multimedia analysis and automatic annotation of semantic multimedia content, combined textual and multimedia information retrieval, semantic -web, MPEG-7 and MPEG-21 standards, user interfaces and human factors. In this paper, recent advances in content-based analysis, indexing and retrieval of digital media within the SCHEMA Network are presented. These advances will be integrated in the SCHEMA module-based, expandable reference system

Fusing MPEG-7 visual descriptors for image classification

This paper proposes three content-based image classification techniques based on fusing various low-level MPEG-7 visual descriptors. Fusion is necessary as descriptors would be otherwise incompatible and inappropriate to directly include e.g. in a Euclidean distance. Three approaches are described: A “merging” fusion combined with an SVM classifier, a back-propagation fusion combined with a KNN classifier and a Fuzzy-ART neurofuzzy network. In the latter case, fuzzy rules can be extracted in an effort to bridge the “semantic gap” between the low-level descriptors and the high-level semantics of an image. All networks were evaluated using content from the repository of the aceMedia project1 and more specifically in a beach/urban scene classification problem