NDLI: Deep Boltzmann Machines based vehicle recognition

Content Provider	IEEE Xplore Digital Library
Author	Aiqin Hu Hong Li Fan Zhang Wei Zhang
Copyright Year	2014
Description	Author affiliation: Sch. of Inf. Sci. & Eng., Central South Univ., Changsha, China (Aiqin Hu; Hong Li; Fan Zhang) \|\| Sch. of Archit., Hunan Univ., Changsha, China (Wei Zhang)
Abstract	We address the problem of image-based vehicle recognition. This problem is formulated as a manifold-feature learning algorithm such that the expected object distribution is evaluated based on the appearance, shape and contours that take into account variational poses and occlusions. We propose to learn manifold hierarchical features where the high-level discriminative features are obtained by capturing correlations among the learned low-level generic features, via a deep learning model, called Deep Boltzmann Machines (DBM), a powerful hierarchical generative model for feature learning. DBM has been proved to be able to explore underlying manifold knowledge of given objects by appropriately describing highly nonlinear complicated functions with novel training process. Moreover, we also present a novel application of deep learning to feature representations, rather than provide the model input with raw image pixels, we instead utilize the human-engineered descriptors such as Log-Gabor, HoG and Gist, as the source data of deep learning architecture. Finally, we evaluate our deep model on PASCAL VOC2012 benchmark dataset, extensive experiments show that our DBM-based model enables to learn useful hierarchical feature representations from few training samples. Specifically, by feeding the DBM-based deep network with prior-described features such as Log-Gabor, HoG and Gist, we get a significant improvement on performance in comparison with the state-of-art results and the best performance is achieved by using a fusion form of the three descriptors as source material for learning deep model. Furthermore, experiments with prior-extracted features as input layer demonstrate that the deep learning networks have provided a perspective of potential ability to learn critical representation of given data, and have presented favorable vehicle recognition performance.
Starting Page	3033
Ending Page	3038
File Size	260953
Page Count	6
File Format	PDF
ISBN	9781479937073
e-ISBN	9781479937080
DOI	10.1109/CCDC.2014.6852695
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-31
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Decision support systems Feature Learning Vehicle Recognition VOC 2012 Dataset Deep Boltzmann Machines Manganese
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

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