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Article overview
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Segmentation Free Object Discovery in Video | Giovanni Cuffaro
; Federico Becattini
; Claudio Baecchi
; Lorenzo Seidenari
; Alberto Del Bimbo
; | Date: |
1 Sep 2016 | Abstract: | In this paper we present a simple yet effective approach to extend without
supervision any object proposal from static images to videos. Unlike previous
methods, these spatio-temporal proposals, to which we refer as tracks, are
generated relying on little or no visual content by only exploiting bounding
boxes spatial correlations through time. The tracks that we obtain are likely
to represent objects and are a general-purpose tool to represent meaningful
video content for a wide variety of tasks. For unannotated videos, tracks can
be used to discover content without any supervision. As further contribution we
also propose a novel and dataset-independent method to evaluate a generic
object proposal based on the entropy of a classifier output response. We
experiment on two competitive datasets, namely YouTube Objects and ILSVRC-2015
VID. | Source: | arXiv, 1609.0221 | Services: | Forum | Review | PDF | Favorites |
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