TY - GEN
T1 - Feedback retargeting
AU - Kav-Venaki, Eitam
AU - Peleg, Shmuel
PY - 2012
Y1 - 2012
N2 - Feedback retargeting combines the benefits of two previous retargeting methods: Bidirectional similarity [1] and Shift-Map [2]. The first method may have blurry areas due to patch averaging and the latter can remove entire objects. Feedback retargeting has the sharpness of shift-map and the completeness of bidirectional similarity, avoiding the removal of salient objects. In Shift-Map retargeting the output image is made from segments of the input image, and this minimizes the forward direction of bidirectional similarity. An iterative feedback procedure is developed to take care of the backward direction, assuring that the input image can be reconstructed from the output image. This is done by using Shift-Map backwards, reconstructing the input image back from the output image. Areas in the input image that are difficult to reconstruct from the output image get a feedback priority score. A second Shift-Map retargeting is then performed, adding this feedback priority to the data term. These regions now have a higher priority to be included in the output. After a few iterations of forward retargeting and backward feedback the retargeted image includes all salient features from the input image. Computational efficiency and image sharpness remain as high as in ordinary Shift-Map.
AB - Feedback retargeting combines the benefits of two previous retargeting methods: Bidirectional similarity [1] and Shift-Map [2]. The first method may have blurry areas due to patch averaging and the latter can remove entire objects. Feedback retargeting has the sharpness of shift-map and the completeness of bidirectional similarity, avoiding the removal of salient objects. In Shift-Map retargeting the output image is made from segments of the input image, and this minimizes the forward direction of bidirectional similarity. An iterative feedback procedure is developed to take care of the backward direction, assuring that the input image can be reconstructed from the output image. This is done by using Shift-Map backwards, reconstructing the input image back from the output image. Areas in the input image that are difficult to reconstruct from the output image get a feedback priority score. A second Shift-Map retargeting is then performed, adding this feedback priority to the data term. These regions now have a higher priority to be included in the output. After a few iterations of forward retargeting and backward feedback the retargeted image includes all salient features from the input image. Computational efficiency and image sharpness remain as high as in ordinary Shift-Map.
UR - http://www.scopus.com/inward/record.url?scp=84871156237&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-35740-4_12
DO - 10.1007/978-3-642-35740-4_12
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AN - SCOPUS:84871156237
SN - 9783642357398
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 145
EP - 155
BT - Trends and Topics in Computer Vision - ECCV 2010 Workshops, Revised Selected Papers
T2 - 11th European Conference on Computer Vision, ECCV 2010
Y2 - 10 September 2010 through 11 September 2010
ER -