LDS-FCM: A Linear Dynamical System Based Fuzzy C-Means Method for Tactile Recognition
- 24 July 2018
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Fuzzy Systems
- Vol. 27 (1), 72-83
- https://doi.org/10.1109/tfuzz.2018.2859184
Abstract
While tactile sensing is becoming an indispensable robotic ability for object recognition and grasping manipulation, it still remains challenging for us to deal with the tactile data as the force distribution over the array sensors continuously changes as a function of time. In this paper, we present an efficient feature extractor named Linear Dynamic Systems-based Fuzzy C-means Method (LDS-FCM) to encode the tactile sequences, both spatially and temporally. To this end, we decompose every input sequence into multiple sub-sequences, each of which is locally described by a finite-ordered observability matrix of the LDS model. A fuzzy c-means method is then applied to cluster the local LDS descriptors for learning a codebook. Conditioned on the resulting codebook, the global tactile representation is formulated by employing two different frameworks to integrate the subsequences within each tactile sequence, namely, the Vector of Locally Aggregated Descriptor (VLAD) and Bag-of-Word (BoW) approaches. The effectiveness of the proposed model is verified by a variety of experimental evaluations on five benchmark datasets. It shows that our proposed method achieves a higher classification accuracy than the state-of-the-art models with a large margin.Keywords
Funding Information
- National Natural Science Foundation of China (61703230, 61621136008, 61327809)
This publication has 46 references indexed in Scilit:
- Design of a flexible tactile sensor for classification of rigid and deformable objectsRobotics and Autonomous Systems, 2014
- Contact-Force Distribution Optimization and Control for Quadruped Robots Using Both Gradient and Adaptive Neural NetworksIEEE Transactions on Neural Networks and Learning Systems, 2013
- A Novel Texture Sensor for Fabric Texture Measurement and ClassificationIEEE Transactions on Instrumentation and Measurement, 2013
- Salient region detection and segmentation for general object recognition and image understandingScience China Information Sciences, 2011
- Optimization method based extreme learning machine for classificationNeurocomputing, 2010
- A simple and fast algorithm for K-medoids clusteringExpert Systems with Applications, 2009
- Dynamic Time WarpingPublished by Springer Science and Business Media LLC ,2007
- $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse RepresentationIEEE Transactions on Signal Processing, 2006
- Binet-Cauchy Kernels on Dynamical Systems and its Application to the Analysis of Dynamic ScenesInternational Journal of Computer Vision, 2006
- Featureless classification of tactile contacts in a gripper using neural networksSensors and Actuators A: Physical, 1997