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Image Analysis for Multimedia Interactive Services, 2007. WIAMIS '07. Eighth International Workshop on

Date 6-8 June 2007

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  • Eight International Workshop on Image Analysis for Multimedia Interactive Services - Cover

    Publication Year: 2007 , Page(s): c1
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  • Eight International Workshop on Image Analysis for Multimedia Interactive Services - Title

    Publication Year: 2007 , Page(s): i - iii
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  • Eight International Workshop on Image Analysis for Multimedia Interactive Services - Copyright

    Publication Year: 2007 , Page(s): iv
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  • Eight International Workshop on Image Analysis for Multimedia Interactive Services - Table of contents

    Publication Year: 2007 , Page(s): v - x
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  • Message from the General Chairs

    Publication Year: 2007 , Page(s): xi
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  • Organizing Committee

    Publication Year: 2007 , Page(s): xii
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  • Sponsors

    Publication Year: 2007 , Page(s): xiii
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  • Recent Advances and Open Issues of Digital Image/Video Search

    Publication Year: 2007 , Page(s): xiv
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (121 KB)  

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  • Composite Object Detection in Video Sequences: Application to Controlled Environments

    Publication Year: 2007 , Page(s): 1
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (204 KB) |  | HTML iconHTML  

    This paper presents a set of techniques for the detection of composite objects in video recordings of a controlled environment. Firstly, a selective region-based analysis is performed by tuning the algorithm to the perceptual characteristics of the object in the environment. Secondly, the controlled perceptual and semantic variabilities of the object are addressed by the detection analysis thanks to a frame by frame update of the object models, and by allowing multiple models for a single object. The proposed techniques are illustrated in the detection of laptops from a zenithal view in a smart room. View full abstract»

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  • Using Decision Trees for Knowledge-Assisted Topologically Structured Data Analysis

    Publication Year: 2007 , Page(s): 2
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (154 KB) |  | HTML iconHTML  

    Supervised learning of an ensemble of randomized trees is considered to recognize classes of events in topologically structured data (e.g. images or time series). We are primarily interested in classification problems that are characterized by severe scarcity of the training samples. The main idea of our paper consists in favoring the selection of attributes that are known to efficiently discriminate the minority class in those nodes of the tree that are close to the leaves and where classes are represented by a small number of training examples. In practice, the knowledge about the ability of an attribute to discriminate the classes represented in a particular node is either provided by an expert or inferred based on a pre-analysis of the entire initial training set. The experimental validation of our approach considers sign language and human behavior recognition. It reveals that the proposed knowledge- assisted tree induction mechanism efficiently compensates for the shortage of the training samples, and significantly improves the tree classifier accuracy in such scenarios. View full abstract»

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  • Event Alignment for Cross-Media Feature Extraction in the Football Domain

    Publication Year: 2007 , Page(s): 3
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (185 KB) |  | HTML iconHTML  

    This paper describes an experiment in creating cross-media descriptors from football-related text and videos. We used video analysis results and combined them with several textual resources - both semi- structured (tabular match reports) and unstructured (textual minute-by-minute match reports). Our aim was to discover the relations among six video data detectors and their behavior during a time window that corresponds to an event described in the textual data. The experiment shows how football events extracted from text can be mapped to corresponding scenes in video and how this may help in extracting event-specific video detectors. View full abstract»

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  • Surveillance Event Interpretation Using Generalized Stochastic Petri Nets

    Publication Year: 2007 , Page(s): 4
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3229 KB) |  | HTML iconHTML  

    In this paper we present video event representation and recognition approaches that are based on Generalized Stochastic Petri Nets (GSPN). Along with the typical modeling capabilities of GSPN for video recognition, we propose to integrate the Petri net marking analysis for better scene understanding. This work focuses on behavior modeling and uses the results of an external module for object detection, tracking and classification. The proposed approach is evaluated using the developed surveillance system which can recognize events from videos and give a textual expression for the detected behavior. The experimental results illustrate the ability of the system to create complex spatiotemporal relations and to recognize the behavior of one or multiple objects in various video scenes. View full abstract»

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  • A Framework for Ontology Enriched Semantic Annotation of CCTV Video

    Publication Year: 2007 , Page(s): 5
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (251 KB) |  | HTML iconHTML  

    This paper deals with the problem of semantic transcoding of CCTV video footage. A framework is proposed that combines Computer Vision algorithms that extract visual semantics, together with Natural Language Processing that automatically builds the domain ontology from unstructured text annotations. The final aim is a system that will link the visual and text semantics in order to routinely annotate video sequences with the appropriate keywords of the domain experts' terminology. View full abstract»

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  • Hidden Markov Models for Video Skim Generation

    Publication Year: 2007 , Page(s): 6
    Cited by:  Papers (5)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (138 KB) |  | HTML iconHTML  

    In this paper we present a statistical framework based on hidden Markov models (HMMs) for video skimming. A chain of HMMs is used to model subsequent story units: HMM states represent different visual-concepts, transitions model the temporal dependencies in each story unit, and stochastic observations are given by single shots. The skim is generated as an observation sequence, where, in order to privilege more informative segments for entering the skim, dynamic shots are assigned higher probability of observation. The effectiveness of the method is demonstrated on a video set from different kinds of programmes, and results are evaluated in terms of metrics that assess the content representational value of the obtained video skims. View full abstract»

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  • A Topology Preserving Approach for Image Classification

    Publication Year: 2007 , Page(s): 7
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (236 KB) |  | HTML iconHTML  

    In this paper, an approach for image analysis and classification is presented. It is based on a topology preserving approach to automatically create a relevance map from salient areas in natural images. It also derives a set of well-structured representations from low-level description to drive the final classification. The backbone of this approach is a distribution mapping strategy involving two basic modules: structured low-level feature extraction using convolution neural network and a topology preservation module based on a growing neural gas network. Classification is achieved by simulating the high-level top-down visual information perception in primates followed by incremental Bayesian parameter estimation. The proposed modular system architecture offers straightforward expansion to include user relevance feedback, contextual input, and multimodal information if available. View full abstract»

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  • Orientation histogram-based matching for region tracking

    Publication Year: 2007 , Page(s): 8
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (581 KB) |  | HTML iconHTML  

    A region tracking technique with particular emphasis on rotation robustness is presented. It is based on region matching divided in two consecutive steps, gradient orientation histogram matching and template matching through normalised cross correlation (NCC). Given the orientation histograms of two image patches, a novel technique is used to estimate the rotation between them together with the similarity. This estimation enhances the performance and speeds-up the process of patch recognition. Fast computation of histograms using the integral histogram approach is exploited. Experiments show a high accuracy in the estimation of location and orientation. View full abstract»

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  • A Directional Texture Descriptor via 2D Walking Ant Histogram

    Publication Year: 2007 , Page(s): 9
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (507 KB) |  | HTML iconHTML  

    A novel texture descriptor, which can be extracted from the major object edges automatically and used for the content-based retrieval in multimedia databases, is presented. The proposed method is adopted from the 2D walking ant histogram, which is in fact a generic shape descriptor recently developed for general purpose multimedia databases. 2D WAH shape descriptor is motivated from the imaginary scenario of a walking ant with a limited line of sight over the boundary of a particular object; eventually each sub- segment is traversed and the process keeps describing a certain line of sight, whether it is a continuous branch or a corner, using individual 2D histograms. In this paper we tuned this approach as an efficient texture descriptor, which achieves a superior performance especially for directional textures. Integrating the whole process as feature extraction module into MUVIS framework allows us to test the mutual performance of the proposed texture descriptor in the context of multimedia indexing and retrieval. View full abstract»

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  • A review of different object recognition methods for the application in driver assistance systems

    Publication Year: 2007 , Page(s): 10
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (133 KB) |  | HTML iconHTML  

    Algorithms in the field of driver assistance have been limited by their requirement for real time in the initial phase of their development. However, as computing power is increasing steadily, new possibilities arise. With focus on this situation a review is presented not defined by its designated field of application in driver assistance systems, but rather by the methods in use, namely video-based object recognition using machine learning. Recent methods are compared in a highly summarized table using criteria such as recognition rate, computational requirements or number of training samples required. Concluding their potential use in driver assistance is discussed. View full abstract»

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  • Automatic Detection and Classification of Traffic Signs

    Publication Year: 2007 , Page(s): 11
    Cited by:  Papers (17)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (283 KB) |  | HTML iconHTML  

    This paper proposes algorithms for the automatic detection of traffic signs from photo or video images and their classification to provide a driver alert system. Several examples taken from Portuguese roads are used to demonstrate the effectiveness of the proposed system. Traffic signs are detected by analyzing color information, notably red and blue, contained on the images. The detected signs are then classified according to their shape characteristics, as triangular, squared and circular shapes. Combining color and shape information, traffic signs are classified into one of the following classes: danger, information, obligation or prohibition. Both the detection and classification algorithms include innovative components to improve the overall system performance. View full abstract»

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  • Using Multiple Domain Visual Context in Image Analysis

    Publication Year: 2007 , Page(s): 12
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1437 KB) |  | HTML iconHTML  

    In this paper we propose an algorithm to improve the results of knowledge-assisted image analysis, based on contextual information. In order to achieve this, we utilize fuzzy algebra, fuzzy sets and relations, towards efficient manipulation of image region concepts. We provide a novel context modelling, based on the OWL language, using RDF reification. Initial image analysis results are enhanced by the utilization of domain-independent, semantic knowledge in terms of concepts and relations between them. The novelty of the presented work is the context-driven re-adjustment of the degrees of confidence of the detected concepts produced by any image analysis technique, utilizing a domain-independent ontology infrastructure to handle the knowledge, as well as multiple application domains. View full abstract»

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  • Discriminative Feature Selection for Applause Sounds Detection

    Publication Year: 2007 , Page(s): 13
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (248 KB) |  | HTML iconHTML  

    The specific sounds such as applause, laughter, explosions, etc. are very helpful to understand high level semantic of audio/video content. The paper focuses on feature selection by evolutional programming for an automatic detection of applause in audio stream. A set of the most discriminative features is selected by Genetic Algorithm and Simulated Annealing. The experiments are run on more than 9 hours of audio selected from various audio and video content. The results show that the applause sound recognition improves if only a few coefficients are selected from MFCC static and dynamic features. Further, the delta-delta coefficients (the 2nd time derivates of MFCCs) highly outperform the delta coefficients. View full abstract»

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  • Global Image Search vs. Regional Search in CBIR Systems

    Publication Year: 2007 , Page(s): 14
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (401 KB) |  | HTML iconHTML  

    The global image search and regional image search are compared by using content-based image retrieval system with user relevance feedback. It was expectable that regional search can minimize the effect of the background to the image retrieval. Images from database are partitioned into regular rectangular regions: 4times4 non-overlapped (NOV) regions and 3times3 overlapped (OV) regions, and a feature vectors are determined for whole images and for regions. Four CBIR scenarios are considered: global search, search based on 4times4 NOV regions, based on 3times3 OV regions and based on arbitrary cropped part of a query image. System is tested over images from Corel IK dataset. View full abstract»

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  • Data-driven and Procedural Analysis and Synthesis of Multimedia

    Publication Year: 2007 , Page(s): xv
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  • Regression-Based Template Tracking in Presence of Occlusions

    Publication Year: 2007 , Page(s): 15
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (183 KB) |  | HTML iconHTML  

    This paper addresses the problem of efficient visual 2D template tracking in the presence of large motions and partial occlusions. We adopt a learning approach, in our case using a Bayesian Mixture of Experts (BME), in which observations at each frame yield direct predictions of the state (e.g. position / scale) of the tracked target. In contrast to other methods in the literature, we explicitly address the problem that the prediction accuracy can deteriorate drastically for observations that are not similar to the ones in the training set; such observations are common in case of partial occlusions or of fast motion. To do so, we couple the BME with a probabilistic kernel-based classifier which, when trained, can determine the probability that a new/unseen observation can accurately predict the state of the target (the 'relevance' of the observation in question). In addition, in the particle filtering framework, we derive a recursive scheme for maintaining an approximation of the posterior probability of the target's state in which the probabilistic predictions of multiple observations are moderated by their corresponding relevance. We apply the algorithm in the problem of 2D template tracking and demonstrate that the proposed scheme outperforms classical methods for discriminative tracking in case of motions large in magnitude and of partial occlusions. View full abstract»

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  • kNN-based high-dimensional Kullback-Leibler distance for tracking

    Publication Year: 2007 , Page(s): 16
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1196 KB) |  | HTML iconHTML  

    This paper deals with region-of-interest (ROI) tracking in video sequences. The goal is to determine in successive frames the region which best matches, in terms of a similarity measure, a ROI defined in a reference frame. Two aspects of such a measure between the reference region and a candidate region can be distinguished: radiometry which indicates if the regions have similar colors and geometry which correlates where these colors are present in the regions. If not using geometry, the number of potential matches increases. A soft geometric constraint can be added in the form of a joint radiometric-geometric PDF. High-dimensional PDF estimation being a difficult problem, measures based on these PDF distances may lead to an incorrect match. Instead, we propose to compute the Kullback-Leibler distance between high-dimensional PDFs without explicit estimation of the PDFs, i.e., directly from the samples using the kth-nearest neighbor (kNN) framework. Results showed accurate tracking. View full abstract»

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