NDLI: Class-specific material categorisation

Content Provider	IEEE Xplore Digital Library
Author	Caputo, B. Hayman, E. Mallikarjuna, P.
Copyright Year	2005
Description	Author affiliation: Lab. of Computational Vision & Active Perception, R. Inst. of Technol., Stockholm (Caputo, B.; Hayman, E.; Mallikarjuna, P.)
Abstract	Although a considerable amount of work has been published on material classification, relatively little of it studies situations with considerable variation within each class. Many experiments use the exact same sample, or different patches from the same image, for training and test sets. Thus, such studies are vulnerable to effectively recognising one particular sample of a material as opposed to the material category. In contrast, this paper places firm emphasis on the capability to generalise to previously unseen instances of materials. We adopt an appearance-based strategy, and conduct experiments on a new database which contains several samples of each of eleven material categories, imaged under a variety of pose, illumination and scale conditions. Together, these sources of intra-class variation provide a stern challenge indeed for recognition. Somewhat surprisingly, the difference in performance between various state-of-the-art texture descriptors proves rather small in this task. On the other hand, we clearly demonstrate that very significant gains can be achieved via different SVM-based classification techniques. Selecting appropriate kernel parameters proves crucial. This motivates a novel recognition scheme based on a decision tree. Each node contains an SVM to split one class from all others with a kernel parameter optimal for that particular node. Hence, each decision is made using a different, optimal, class-specific metric. Experiments show the superiority of this approach over several state-of-the-art classifiers
Sponsorship	IEEE Comput. Soc. Tech. Comm. on Pattern Anal. and Machine Intelligence
Starting Page	1597
Ending Page	1604
File Size	318495
Page Count	8
File Format	PDF
ISBN	076952334X
ISSN	15505499
DOI	10.1109/ICCV.2005.54
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-10-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Conducting materials Image databases Lighting Kernel Robots Pattern recognition Image recognition Computer vision Laboratories Computer science
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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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
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