Anticipating the oncoming integration of depth sensing into mobile devices, we experimentally compare different compact features for representing RGB-D images in mobile visual search. Experiments on 3 state-of-the-art datasets, addressing both category and instance recognition, show how Deep Features provided by Convolutional Neural Networks better represent appearance information, whereas shape is more effectively encoded through Kernel Descriptors. Moreover, our evaluation suggests that learning to weight the relative contribution of depth and appearance is key to deploy effectively depth sensing in forthcoming mobile visual search scenarios.
Analysis of compact features for RGB-D visual search / Petrelli, Alioscia; Pau, Danilo; Di Stefano, Luigi. - STAMPA. - 9280:(2015), pp. 14-24. (Intervento presentato al convegno 18th International Conference on Image Analysis and Processing, ICIAP 2015 tenutosi a ita nel 2015) [10.1007/978-3-319-23234-8_2].
Analysis of compact features for RGB-D visual search
PETRELLI, ALIOSCIA;DI STEFANO, LUIGI
2015
Abstract
Anticipating the oncoming integration of depth sensing into mobile devices, we experimentally compare different compact features for representing RGB-D images in mobile visual search. Experiments on 3 state-of-the-art datasets, addressing both category and instance recognition, show how Deep Features provided by Convolutional Neural Networks better represent appearance information, whereas shape is more effectively encoded through Kernel Descriptors. Moreover, our evaluation suggests that learning to weight the relative contribution of depth and appearance is key to deploy effectively depth sensing in forthcoming mobile visual search scenarios.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
