Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Decision Theory: An Introduction to Dynamic Programming and Sequential Decisions
Decision Theory: An Introduction to Dynamic Programming and Sequential Decisions
Probabilistic models of indexing and searching
SIGIR '80 Proceedings of the 3rd annual ACM conference on Research and development in information retrieval
The combination limit in multimedia retrieval
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Large-Scale Concept Ontology for Multimedia
IEEE MultiMedia
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
The challenge problem for automated detection of 101 semantic concepts in multimedia
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Introduction to Probability Models, Ninth Edition
Introduction to Probability Models, Ninth Edition
Evaluation of active learning strategies for video indexing
Image Communication
A note on Platt's probabilistic outputs for support vector machines
Machine Learning
A review of text and image retrieval approaches for broadcast news video
Information Retrieval
Probabilistic models for combining diverse knowledge sources in multimedia retrieval
Probabilistic models for combining diverse knowledge sources in multimedia retrieval
(Un)Reliability of video concept detection
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
A probabilistic ranking framework using unobservable binary events for video search
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Foundations and Trends in Information Retrieval
Measuring the Influence of Concept Detection on Video Retrieval
CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
Reusing annotation labor for concept selection
Proceedings of the ACM International Conference on Image and Video Retrieval
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
A comparison of score, rank and probability-based fusion methods for video shot retrieval
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
The use and utility of high-level semantic features in video retrieval
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
Using high-level semantic features in video retrieval
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Can High-Level Concepts Fill the Semantic Gap in Video Retrieval? A Case Study With Broadcast News
IEEE Transactions on Multimedia
Representations of Keypoint-Based Semantic Concept Detection: A Comprehensive Study
IEEE Transactions on Multimedia
Using visual lifelogs to automatically characterize everyday activities
Information Sciences: an International Journal
The uncertain representation ranking framework for concept-based video retrieval
Information Retrieval
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In this paper we address the following important questions for concept-based video retrieval: (1) What is the impact of detector performance on the performance of concept-based retrieval engines, and (2) will these engines be applicable to real-life search tasks if detector performance improves in the future? We use Monte Carlo simulations to answer these questions. To generate the simulation input, we propose to use a probabilistic model of two Gaussians for the confidence scores that concept detectors emit. Modifying the model's parameters affects the detector performance and the search performance. We study the relation between these two performances on two video collections. For detectors with similar discriminative power and a concept vocabulary of around 100 concepts, the simulation reveals that in order to achieve a search performance of 0.20 mean average precision (MAP)--which is considered sufficient performance for real-life applications--one needs detectors with at least 0.60 MAP . We also find that, given our simulation model and low detector performance, MAP is not always a good evaluation measure for concept detectors since it is not strongly correlated with the search performance.