NDLI: Preprocessing-free surface material classification using convolutional neural networks pretrained by sparse Autoencoder

Content Provider	IEEE Xplore Digital Library
Author	Mengqi Ji Lu Fang Haitian Zheng Strese, M. Steinbach, E.
Copyright Year	2015
Description	Author affiliation: Hong Kong Univ. of Sci. & Technol., Hong Kong, China (Mengqi Ji; Lu Fang) \|\| Tech. Univ. Munchen, Munich, Germany (Strese, M.; Steinbach, E.) \|\| Univ. of Sci. & Technol. of China, Hefei, China (Haitian Zheng)
Abstract	Acceleration signals captured during the interaction of a rigid tool with an object surface carry relevant information for surface material classification. Existing methods mostly rely on carefully designed perception-related features or features adapted from audio processing motivated by the observed similarity between acceleration signals and audio signals. In contrast, our proposed method automatically learns features from RAW acceleration data without preprocessing. The approach is based on Convolutional Neural Networks (CNN) trained and tested on RAW data. For better performance and faster convergence of the CNN, we use the weights of a trained sparse Autoencoder (AE) to initialize the weights of the first convolution layers of the CNN. This strategy is named CNN pretrained by sparse AE (ACNN). Our classification results on a publically available Haptic Texture Database demonstrate that the proposed algorithm performs favorably against existing methods.
Starting Page	1
Ending Page	6
File Size	430983
Page Count	6
File Format	PDF
ISBN	9781467374545
DOI	10.1109/MLSP.2015.7324324
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-17
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Convolution Acceleration Feature extraction Kernel Haptic interfaces Training Transforms sparse Autoencoder haptic texture classification convolutional neural networks CNN pretraining
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Convolutional autoencoder-based color image classification using chroma subsampling in YCbCr space

Correlative Filters for Convolutional Neural Networks

Microvascular morphological type recognition using trainable feature extractor

Multistage committees of deep feedforward convolutional sparse denoise autoencoder for object recognition

Vehicle Type Classification Using Unsupervised Convolutional Neural Network

Research on a Convolution Kernel Initialization Method for Speeding Up the Convergence of CNN

Hybrid multi-layer deep CNN/aggregator feature for image classification

Recreating Fingerprint Images by Convolutional Neural Network Autoencoder Architecture

Transfer learning between texture classification tasks using Convolutional Neural Networks

Preprocessing-free surface material classification using convolutional neural networks pretrained by sparse Autoencoder

Similar Documents

Convolutional autoencoder-based color image classification using chroma subsampling in YCbCr space

Correlative Filters for Convolutional Neural Networks

Microvascular morphological type recognition using trainable feature extractor

Multistage committees of deep feedforward convolutional sparse denoise autoencoder for object recognition

Vehicle Type Classification Using Unsupervised Convolutional Neural Network

Research on a Convolution Kernel Initialization Method for Speeding Up the Convergence of CNN

Hybrid multi-layer deep CNN/aggregator feature for image classification

Recreating Fingerprint Images by Convolutional Neural Network Autoencoder Architecture

Transfer learning between texture classification tasks using Convolutional Neural Networks

Preprocessing-free surface material classification using convolutional neural networks pretrained by sparse Autoencoder