NDLI: “Bag of visual words” and latent semantic analysis-based burning state recognition for rotary kiln sintering process

Content Provider	IEEE Xplore Digital Library
Author	Weitao Li Xiaojie Zhou Tianyou Chai
Copyright Year	2011
Description	Author affiliation: Key Laboratory of Integrated Automation of Process Industry, Ministry of Education, Northeastern University, Shenyang, 110004, China (Weitao Li; Xiaojie Zhou; Tianyou Chai)
Abstract	For the sintering process of rotary kiln, the accurate recognition of burning zone state is considered to be the most critical issue. Due to the harsh environment inside the kiln and the limitation of the measuring device, the measurement is still a challenging task. Recently, flame image-based state recognition has received considerate attention. However, the recognition accuracy of previous image segmentation-based methods is hard to guarantee due to the disturbance from smoke and dust. In this study, a new method for burning state recognition without the need of image segmentation is proposed, with the goal of achieving more reliable state recognition. Firstly, scale invariant feature transform (SIFT) operator is employed to extract key feature points of flame image, and then “bag of visual words” is applied to vector quantize the SIFT descriptors, and term frequency-inverse document frequency weight is used to form the indexing table to reduce the dimensionality of feature representation. After obtaining such table, latent semantic analysis (LSA) is used to map the original “images-visual words” space to a latent semantic space to mitigate the problem of synonymy. Previously, very little attention has been paid to the saliency of topics. In our work, a topic selection procedure based on Mahalanobis separability measure is proposed, with the goal of making up the lack of location information to select topics that possess the maximum discriminative power to enhance classification performance. The contribution of our new burning state recognition method is threefold. Firstly, SIFT descriptor is robust to characterize local zones of flame image than the features extracted from image segmentation-based methods. Secondly, “bag of visual words” representation for flame images combined with LSA is feasible to recognize the burning state which has never been used before. Thirdly, our topic selection approach is not only to generate a more meaningful topic subset, but also to improve classification performance. The proposed new method is validated through extensive experimental studies.
Starting Page	377
Ending Page	382
File Size	385873
Page Count	6
File Format	PDF
ISBN	9781424487370
e-ISBN	9781424487387
DOI	10.1109/CCDC.2011.5968206
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-23
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Temperature measurement Visualization Kilns Image segmentation Image recognition bag of visual words Semantics topic selection Feature extraction latent semantic analysis burning state
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