Preliminary Exploration of the Use of Geographical Information for Content-based Geo-tagging of Social Video
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This document presents a system that aims to derive location information from visual content in videos without geo-tags. It divides the world into regions based on different criteria like temperature and biomes and uses visual similarity measures to match videos to these regions. Initial tests on 500 videos using a 22 biome classification achieved a 12.17% accuracy, better than random chance of 4.55%. Future work will focus on only using outdoor videos and excluding indoor images which provide noisy information.
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fingerprint in the corresponding fingerprint database is searched using inverted-file-based method.
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Closing
The document summarizes the MediaEval 2012 Multimedia Benchmark Workshop held in Pisa, Italy on October 5, 2012. It discusses the "less is more" philosophy of the workshop, which forces participants to focus on the essentials with only 5 runs, 2 pages, and 10 minutes. It describes how MediaEval is a group dedicated to advancing multimedia state-of-the-art by focusing efforts on collaborative tasks. Finally, it provides information on the timeline and process for MediaEval 2013.
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Search and Hyperlinking Task at MediaEval 2012
This document summarizes the Search and Hyperlinking Task at the MediaEval 2012 evaluation. It describes the two subtasks of search and linking of videos using automatic speech recognition transcripts and video clues. Participants were evaluated on their ability to search and retrieve relevant video segments based on queries, and to link related video segments for given anchor videos. The task aimed to provide a unified scenario for search and linking using crowdsourcing to assess results relevance. Several research groups participated by submitting runs for the search and linking subtasks.
CUNI at MediaEval 2012: Search and Hyperlinking Task
The document summarizes CUNI's participation in the Search and Hyperlinking Task at MediaEval 2012. It describes their use of two transcription files, two segmentation strategies (regular time-based and TextTiling), and the Terrier information retrieval system. Their highest scores for MRR and mGAP measures used regular 90-second segments, while TextTiling achieved the highest MASP scores. Shorter regular segments performed better for mGAP and MASP, while MRR peaked at 90 seconds. Future work could improve segmentation precision and incorporate audio/visual features.
DCU Search Runs at MediaEval 2012: Search and Hyperlinking Task
This document summarizes the DCU Search runs submitted to the MediaEval 2012 Search and Hyperlinking task. It describes the segmentation method used, which segmented audio into 180 second segments with 60 seconds of overlap and included pauses. It also provides details on the sub-corpora used, including the number of documents and terms for runs using the LIUM and LIMSI transcripts with and without pause segmentation and overlap. The runs used the Terrier IR system with a standard language model.
Ghent University-IBBT at MediaEval 2012 Search and Hyperlinking: Semantic Sim...
1. The document discusses an approach for the MediaEval 2012 Search and Hyperlinking task that creates an enriched representation of videos and queries, applies multiple similarity metrics, and merges results through late fusion.
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Brave New Task: User Account Matching
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This document provides an overview of a visual privacy task that aims to obscure or scramble regions of people in videos in an acceptable and attractive way while protecting their identity. The task involves 200 test videos ranging from 30-90 seconds long and uses an evaluation set of 12 videos to objectively and subjectively evaluate three approaches based on accuracy, anonymity, intelligibility, quality, and user study questionnaires. The results show that the University of Reading approach had the best performance based on the objective metrics and user study feedback.
MediaEval 2012 Visual Privacy Task: Privacy and Intelligibility through Pixel...
This document discusses privacy protection through visual anonymization techniques. It summarizes research on pixellating and edge detection filters to anonymize faces while maintaining image intelligibility. Two main challenges are detecting faces and filtering them privately. The researchers evaluate face detection algorithms and combine them with tracking and discrimination of front and back faces. They also describe pixellating and edge detection filters and their objective and subjective evaluation, finding the filters protect privacy to some extent but with room for improvement in detection accuracy and reducing filter irritation.
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1) The document describes research on detecting violent scenes using large sets of acoustic and visual features extracted from video frames.
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mevd2012 esra_
The document describes a method for detecting violent scenes in movies using affective audio and visual features. Low-level audio and visual features are extracted from movie segments and used to generate mid-level audio representations based on Bag of Audio Words. A two-class SVM is then trained on the fused audio and visual features to classify segments as violent or non-violent. Evaluation on three movies showed slightly better performance using mid-level representations compared to low-level features alone. However, the author concludes that more sophisticated affect-related features are needed, especially for visual representations, to better describe violent video content.
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This document summarizes the results of 5 different systems for detecting violent scenes in videos that were submitted by Technicolor, INRIA, and Imperial College London to the MediaEval 2012 Violent Scene Detection Task. System 1 used similarity measures between frames, System 2 used bag-of-audio-words modeling, System 3 used Bayesian network structure learning, System 4 used a naive Bayesian classifier, and System 5 fused the outputs of the first 3 systems. System 3 performed best with a MAP of 61.82% while the fusion system was the 4th best overall system. The document concludes with perspectives on improving the different approaches.
The MediaEval 2012 Affect Task: Violent Scenes Detectio
This document summarizes the MediaEval 2012 violent scenes detection task. The goal is to detect violent segments in movies to help users choose child-appropriate content. Violence is defined as physical injury or pain. Participants were provided 18 movies annotated at the shot-level for violence. Runs detecting violence at the shot and segment level were submitted. Evaluation used mean average precision and a cost-based metric. 11 teams from 9 countries participated, submitting 36 runs total. The best performing run achieved a MAP@100 of 65.05. Participation was higher than the previous year with more joint submissions and workshop attendance.
NII, Japan at MediaEval 2012 Violent Scenes Detection Affect Task
This document summarizes the approaches taken by researchers from the National Institute of Informatics (NII) in Japan for the violent scenes detection task at MediaEval 2012. They used the NII-KAORI-SECODE framework to extract shot-based features from 5 keyframes per shot, including color moments, color histograms, edge orientation histograms and local binary patterns. They evaluated late fusion with visual attributes and used LibSVM for classifier learning. Their discussion noted challenges with the broad definition of violence and diversity of shot lengths.
LIG at MediaEval 2012 affect task: use of a generic method
The document summarizes the LIG Quaero consortium's system for detecting violent scenes in videos submitted to the MediaEval 2012 evaluation. The system uses a hierarchical classification pipeline that extracts multiple audio, visual, and motion descriptors from video shots. It applies optimization techniques to the descriptors and uses multiple support vector machine and k-nearest neighbor classifiers. The classifiers and descriptor variants are fused at different levels of the hierarchy. Techniques like conceptual feedback and temporal re-ranking are used to improve performance. The best performing run achieved a MAP@100 score of 0.3138 on the test data.
Violence Detection in Video by Large Scale Multi-Scale Local Binary Pattern D...
The document discusses a multi-scale local binary pattern (MSLBP) dynamics approach for detecting violence in video streams. It downsamples video to 5Hz, generates an MSLBP vector for each image, and computes the average and variance of changes in M over 20 second sections. A linear SVM is trained on the 20480 dimensions to classify sections as violent or not. Results on one movie were misleading due to feature errors, and the model struggles with desynchronization, but can distinguish fast movements from violent events.
ARF @ MediaEval 2012: An Uninformed Approach to Violence Detection in Hollywo...
The document summarizes an approach to detecting violence in movies using machine learning. It extracts low-level audio and visual features from movies and trains a classifier to predict mid-level concepts like blood, fire, and screams. These predictions are then used to train a second classifier to predict violence on a frame-by-frame basis. Experimental results show this two-stage approach achieved a precision of 0.28 and recall of 0.49 for segment-level violence prediction on test movies. Future work could investigate using different modalities and regularization techniques to improve the results.
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- 1. Preliminary Exploration of the Use of Geographical Information for Content- based Geo-tagging of Social Video 5-10-2012 Xinchao Li, Claudia Hauff, Martha Larson, Alan Hanjalic Delft University of Technology Challenge the future
- 2. System Overview • Goal derive location information from the visual content of videos • Challenge • no tags: 35.7%, only one tag: 13.1% • improve metadata-based system System Overview Visual similarity measures for semantic video retrieval 2
- 3. •Assumption divide the world map into regions that have a high within- region visual stability and a high between-region variability South Pole Great Victoria Desert System Overview Visual similarity measures for semantic video retrieval 3
- 4. Different Division Methods • Baseline Visual similarity measures for semantic video Methods Different Division retrieval 4
- 5. • Temperature Data based Visual similarity measures for semantic video Methods Different Division retrieval 5
- 6. • Temperature Data based 6 temperature regions: from -20◦C to 40◦C with 10◦C intervals. Visual similarity measures for semantic video Methods Different Division retrieval 6
- 7. • Biomes Data based Visual similarity measures for semantic video Methods Different Division retrieval 7
- 8. Run Results Run Results Visual similarity measures for semantic video retrieval 8
- 9. Run Results 22 Biomes classification: 12.17% (random, 4.55%) Run Results Visual similarity measures for semantic video retrieval 9
- 10. Discussion • Visual Content of Test Videos 500 videos from the 4182 videos (12%) • Indoor (42%) • Outdoor Event (32%) • Normal Outdoor (26%) • Visual Content of Training Photos 458 photos from the 3M training set • Indoor (27.5%) Discussion Visual similarity measures for semantic video retrieval 10
- 11. Indoor (42%) Discussion Visual similarity measures for semantic video retrieval 11
- 12. Outdoor Event (32%) Discussion Visual similarity measures for semantic video retrieval 12
- 13. Normal (26%) Discussion Visual similarity measures for semantic video retrieval 13
- 14. Conclusion and Future work • Recall our assumption “we can divide the world map into regions that have a high within-region visual stability and a high between-region variability.” • indoor images are noisy information • Only use outdoor videos to train and test Discussion Visual similarity measures for semantic video retrieval 14
- 15. Thank you! X.Li-3@tudelft.nl Visual similarity measures for semantic video retrieval 15