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TUTCRIS

Weighted Linear Discriminant Analysis Based on Class Saliency Information

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko2018 25th IEEE International Conference on Image Processing (ICIP)
KustantajaIEEE
ISBN (elektroninen)978-1-4799-7061-2
DOI - pysyväislinkit
TilaJulkaistu - 2018
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE International Conference on Image Processing -
Kesto: 7 lokakuuta 201810 lokakuuta 2018

Julkaisusarja

Nimi
ISSN (elektroninen)2381-8549

Conference

ConferenceIEEE International Conference on Image Processing
Ajanjakso7/10/1810/10/18

Tiivistelmä

In this paper, we propose a new variant of Linear Discriminant Analysis to overcome underlying drawbacks of traditional LDA and other LDA variants targeting problems involving imbalanced classes. Traditional LDA sets assumptions related to Gaussian class distribution and neglects influence of outlier classes, that might hurt in performance. We exploit intuitions coming from a probabilistic interpretation of visual saliency estimation in order to define saliency of a class in multi-class setting. Such information is then used to redefine the between-class and within-class scatters in a more robust manner. Compared to traditional LDA and other weight-based LDA variants, the proposed method has shown certain improvements on facial image classification problems in publicly available datasets.

Julkaisufoorumi-taso