Visual Saliency by Extended Quantum Cuts
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Yksityiskohdat
| Alkuperäiskieli | Englanti |
|---|---|
| Otsikko | 2015 IEEE International Conference on Image Processing (ICIP) |
| Kustantaja | IEEE SIGNAL PROCESSING SOCIETY |
| Sivut | 1692-1696 |
| Sivumäärä | 5 |
| ISBN (painettu) | 978-1-4799-8339-1 |
| DOI - pysyväislinkit | |
| Tila | Julkaistu - 2015 |
| OKM-julkaisutyyppi | A4 Artikkeli konferenssijulkaisussa |
| Tapahtuma | IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING - Kesto: 1 tammikuuta 1900 → … |
Conference
| Conference | IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING |
|---|---|
| Ajanjakso | 1/01/00 → … |
Tiivistelmä
In this study, we propose an unsupervised, state-of-the-art saliency map generation algorithm which is based on a recently proposed link between quantum mechanics and spectral graph clustering, Quantum Cuts. The proposed algorithm forms a graph among superpixels extracted from
an image and optimizes a criterion related to the image boundary, local contrast and area information. Furthermore, the effects of the graph connectivity, superpixel shape irregularity, superpixel size and how to determine the affinity between superpixels are analyzed in detail. Furthermore, we introduce a novel approach to propose several saliency maps. Resulting saliency maps consistently achieves a state-of-the-art performance in a large number of publicly available benchmark datasets in this domain, containing
around 18k images in total.
an image and optimizes a criterion related to the image boundary, local contrast and area information. Furthermore, the effects of the graph connectivity, superpixel shape irregularity, superpixel size and how to determine the affinity between superpixels are analyzed in detail. Furthermore, we introduce a novel approach to propose several saliency maps. Resulting saliency maps consistently achieves a state-of-the-art performance in a large number of publicly available benchmark datasets in this domain, containing
around 18k images in total.