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TUTCRIS

Salient Object Segmentation based on Linearly Combined Affinity Graphs

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko2016 23rd International Conference on Pattern Recognition (ICPR)
KustantajaIEEE
ISBN (elektroninen)978-1-5090-4847-2
DOI - pysyväislinkit
TilaJulkaistu - 2017
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaINTERNATIONAL CONFERENCE ON PATTERN RECOGNITION -
Kesto: 1 tammikuuta 1900 → …

Conference

ConferenceINTERNATIONAL CONFERENCE ON PATTERN RECOGNITION
Ajanjakso1/01/00 → …

Tiivistelmä

In this paper, we propose a graph affinity learning method for a recently proposed graph-based salient object detection method, namely Extended Quantum Cuts (EQCut). We exploit the fact that the output of EQCut is differentiable with respect to graph affinities, in order to optimize linear combination coefficients and parameters of several differentiable affinity functions by applying error backpropagation. We show that the learnt linear combination of affinities improves the performance over the baseline method and achieves comparable (or even better) performance when compared to the state-of-the-art salient object segmentation methods.

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