PT Unknown
AU Marc Bolaños
   Maite Garolera
   Petia Radeva
TI Object Discovery using CNN Features in Egocentric Videos
BT Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015
PY 2015
BP 67
EP 74
VL 9117
DI 10.1007/978-3-319-19390-8_8
DE Object discovery; Egocentric videos; Lifelogging; CNN
AB Lifelogging devices based on photo/video are spreading faster everyday. This growth can represent great benefits to develop methods for extraction of meaningful information about the user wearing the device and his/her environment. In this paper, we propose a semi-supervised strategy for easily discovering objects relevant to the person wearing a first-person camera. The egocentric video sequence acquired by the camera, uses both the appearance extracted by means of a deep convolutional neural network and an object refill methodology that allow to discover objects even in case of small amount of object appearance in the collection of images. We validate our method on a sequence of 1000 egocentric daily images and obtain results with an F-measure of 0.5, 0.17 better than the state of the art approach.
ER