Analysis of compact features for RGB-D visual search

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.