Machine Learning
Machine Learning
On the detection of semantic concepts at TRECVID
Proceedings of the 12th annual ACM international conference on Multimedia
Multimodal Video Indexing: A Review of the State-of-the-art
Multimedia Tools and Applications
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
Evaluation of active learning strategies for video indexing
Image Communication
Get another label? improving data quality and data mining using multiple, noisy labelers
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Cheap and fast---but is it good?: evaluating non-expert annotations for natural language tasks
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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In this paper, we have described the Active Cleaning approach that was used to complete the active learning approach in the TRECVID collaborative annotation. It consists of using a classification system to select the samples to be re-annotated in order to improve the quality of the annotations. We have evaluated the actual impact of our active cleaning approach on the TRECVID 2007 collection, using full annotations collected from the TRECVID collaborative annotations and the MCG-ICT-CAS annotations. From our experiments, a significant improvement of our annotation systems performance was observed when selecting a small fraction of samples to be re-annotated by our cleaning strategy, denoted as Cross-Val , than using the same fraction to annotate more new samples. Furthermore, it shows that higher performance can be reached with double annotations of 10% of negative samples, or 5% of all the annotated samples that were selected by the proposed cleaning strategy.