Unsupervised scene classification based on context of features for a mobile robot

Authors:
Hirokazu Madokoro;Yuya Utsumi;Kazuhito Sato
Affiliations:
Department of Machine Intelligence and Systems Engineering, Akita Prefectural University, Yurihonjo City, Akita, Japan;Department of Machine Intelligence and Systems Engineering, Akita Prefectural University, Yurihonjo City, Akita, Japan;Department of Machine Intelligence and Systems Engineering, Akita Prefectural University, Yurihonjo City, Akita, Japan
Venue:
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part I
Year:
2011

Citing 8
Cited 0

Self-Organizing Maps

Self-Organizing Maps
An Experimental and Theoretical Investigation into Simultaneous Localisation and Map Building

The Sixth International Symposium on Experimental Robotics VI
Object Recognition from Local Scale-Invariant Features

ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Context-based vision system for place and object recognition

ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Rapid Biologically-Inspired Scene Classification Using Features Shared with Visual Attention

IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual place categorization: problem, dataset, and algorithm

IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Overview of the CLEF 2009 robot vision track

CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
CENTRIST: A Visual Descriptor for Scene Categorization

IEEE Transactions on Pattern Analysis and Machine Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents an unsupervised scene classification method based on the context of features for semantic recognition of indoor scenes used for an autonomous mobile robot. Our method creates Visual Words (VWs) of two types using Scale-Invariant Feature Transform (SIFT) and Gist. Using the combination of VWs, our method creates Bags of VWs (BoVWs) to vote for a two-dimensional histogram as context-based features. Moreover, our method generates labels as a candidate of categories while maintaining stability and plasticity together using the incremental learning function of Adaptive Resonance Theory-2 (ART-2). Our method actualizes unsupervised-learning-based scene classification using generated labels of ART-2 as teaching signals of Counter Propagation Networks (CPNs). The spatial and topological relations among scenes are mapped on the category map of CPNs. The relations of classified scenes that include categories are visualized on the category map. The experiment demonstrates the classification accuracy of semantic categories such as office rooms and corridors using an open dataset as an evaluation platform of position estimation and navigation for an autonomous mobile robot.