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Regular Research Article 06 Aug 2013 Unsupervised semantic indoor scene classification for robot vision based on context of features using Gist and HSV-SIFT H. Madokoro, A. Yamanashi, and K. SatoFaculty of Systems Science and Technology, Akita Prefectural University, Akita, Japan
Received: 27 May 2013 – Revised: 29 Jul 2013 – Accepted: 01 Aug 2013 – Published: 06 Aug 2013 Abstract. This paper presents an unsupervised scene classification method for
actualizing semantic recognition of indoor scenes. Background and foreground
features are respectively extracted using Gist and color scale-invariant
feature transform (SIFT) as feature representations based on context. We used
hue, saturation, and value SIFT (HSV-SIFT) because of its simple algorithm
with low calculation costs. Our method creates bags of features for voting
visual words created from both feature descriptors to a two-dimensional
histogram. Moreover, our method generates labels as candidates of categories
for time-series images while maintaining stability and plasticity together.
Automatic labeling of category maps can be realized using labels created
using adaptive resonance theory (ART) as teaching signals for counter
propagation networks (CPNs). We evaluated our method for semantic scene
classification using KTH's image database for robot localization (KTH-IDOL),
which is popularly used for robot localization and navigation. The mean
classification accuracies of Gist, gray SIFT, one class support vector
machines (OC-SVM), position-invariant robust features (PIRF), and our method
are, respectively, 39.7, 58.0, 56.0, 63.6, and 79.4%. The
result of our method is 15.8% higher than that of PIRF. Moreover, we
applied our method for fine classification using our original mobile robot.
We obtained mean classification accuracy of 83.2% for six zones.
Citation: Madokoro, H., Yamanashi, A., and Sato, K.: Unsupervised semantic indoor scene classification for robot vision based on context of features using Gist and HSV-SIFT, Pattern Recogn. Phys., 1, 93-103, doi:10.5194/prp-1-93-2013, 2013.
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