NDLI: A framework for TV logos learning using linear inverse diffusion filters for noise removal

Content Provider	IEEE Xplore Digital Library
Author	Cozar, J.R. Zeljkovic, V. Gonzalez-Linares, J.M. Guil, N. Tameze, C. Valev, V.
Copyright Year	2013
Description	Author affiliation: Dept. of Math. & Comput. Sci., Lincoln Univ., Lincoln, PA, USA (Tameze, C.) \|\| Comput. Archit. Dept., Univ. of Malaga, Malaga, Spain (Cozar, J.R.; Gonzalez-Linares, J.M.; Guil, N.) \|\| Sch. of Eng. & Comput. Sci., New York Inst. of Technol., New York, NY, USA (Zeljkovic, V.) \|\| Inst. of Math. & Inf., Sofia, Bulgaria (Valev, V.)
Abstract	Different logotypes represent significant cues for video annotations. A combination of temporal and spatial segmentation methods can be used for logo extraction from various video contents. To achieve this segmentation, pixels with low variation of intensity over time are detected. Static backgrounds can become spurious parts of these logos. This paper offers a new way to use several segmentations of logos to learn new logo models from which noise has been removed. First, we group segmented logos of similar appearances into different clusters. Then, a model is learned for each cluster that has a minimum number of members. This is done by applying a linear inverse diffusion filter to all logos in each cluster. Our experiments demonstrate that this filter removes most of the noise that was added to the logo during segmentation and it successfully copes with misclassified logos that have been wrongly added to a cluster.
Starting Page	621
Ending Page	625
File Size	815916
Page Count	5
File Format	PDF
ISBN	9781479908363
e-ISBN	9781479908387
DOI	10.1109/HPCSim.2013.6641479
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-01
Publisher Place	Finland
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Maximum likelihood detection Image segmentation TV Shape Linear inverse diffusion filter Noise Nonlinear filters Logotype Video segmentation Noise measurement Clustering
Content Type	Text
Resource Type	Article

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