Evaluation of the SIFT Object Recognition Method in Mobile Robots

CCIA 2009. 12th International Conference of the Catalan Association for Artificial Intelligence, Cardona, Spain, 21-23 October, 2009General object recognition in mobile robots is of primary importance in order to enhance the representation of the environment that robots will use for their reasoning processes. Therefore, we contribute reduce this gap by evaluating the SIFT Object Recognition method in a challenging dataset, focusing on issues relevant to mobile robotics. Resistance of the method to the robotics working conditions was found, but it was limited mainly to well-textured objects.This work was supported by the FI grant from the Generalitat de Catalunya, the European Social Fund, and the MID-CBR project grant TIN2006-15140-C03-01 and FEDER funds and the grant 2005-SGR-00093 and the MIPRCV Consolider Imagennio 2010.Peer reviewe

Mutual Information Based Learning Rate Decay for Stochastic Gradient Descent Training of Deep Neural Networks

This paper demonstrates a novel approach to training deep neural networks using a Mutual Information (MI)-driven, decaying Learning Rate (LR), Stochastic Gradient Descent (SGD) algorithm. MI between the output of the neural network and true outcomes is used to adaptively set the LR for the network, in every epoch of the training cycle. This idea is extended to layer-wise setting of LR, as MI naturally provides a layer-wise performance metric. A LR range test determining the operating LR range is also proposed. Experiments compared this approach with popular alternatives such as gradient-based adaptive LR algorithms like Adam, RMSprop, and LARS. Competitive to better accuracy outcomes obtained in competitive to better time, demonstrate the feasibility of the metric and approach

A Bayesian Conceptualization of Space for Mobile Robots

Abstract — The future of robots, as our companions is dependent on their ability to understand, interpret and represent the environment in a human compatible manner. Towards this aim of making robots more spatially cognizant, the presented work is part of an attempt to create a hierarchical probabilistic concept-oriented representation of space, based on objects. Specifically, this work details efforts taken towards learning and generating concepts from the perceived objects and attempts to classify places using the concepts gleaned. The approach is based on learning from exemplars, clustering and the use of Bayesian network classifiers. Experiments on conceptualization and place classification are reported. Thus, the theme of the work is- conceptualization and classification for representation and spatial cognition. I

A Bayesian Approach to Conceptualization and Place Classification: Incorporating Spatial Relationships (distances) to Infer Concepts

The future of robots, as our companions is dependent on their ability to understand, interpret and represent the environment in a human compatible manner. Towards this aim, the presented work is part of an attempt to create a hierarchical probabilistic concept-oriented representation of space, based on objects. Specifically, this work details efforts taken towards learning and generating concepts and attempts to classify places using the concepts gleaned. Inference is based on the number of occurrences of various objects. The approach is based on learning from exemplars, clustering and the use of Bayesian network classifiers. Such a conceptualization and the representation that results thereof, will enable robots to be more cognizant of their surroundings and yet, compatible to us. Experiments on conceptualization and place classification are reported. Thus, the theme of the work is- conceptualization and classification for representation and spatial cognitio