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Semantic Place Recognition based on Deep Belief Networks and Tiny Images

Authors: 
Ahmad Hasasneh
Emmanuelle Frenoux
Philippe Tarroux
Conference: 
ICINCO
Date: 
Sunday, January 1, 2012
Abstract: 
This paper presents a novel approach for robot semantic place recognition (SPR) based on Restricted Boltzmann Machines (RBMs) and a direct use of tiny images. RBMs are able to code images as a superposition of a limited number of features taken from a larger alphabet. Repeating this process in a deep architecture leads to an efficient sparse representation of the initial data in the feature space. A complex problem of classification in the input space is thus transformed into an easier one in the feature space. In this article, we show that SPR can thus be achieved using tiny images instead of conventional Bag-of-Words (BoW) methods. After appropriate coding, a softmax regression in the feature space suffices to compute the probability to be in a given place according to the input image.
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