A Novel Approach for Classification of Indoor Scenes
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International Journal of Computer Trends and Technology (IJCTT) | 
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| © 2015 by IJCTT Journal | ||
| Volume-23 Number-3 |
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| Year of Publication : 2015 | ||
| Authors : Gagandeep Kaur, Dr. Amandeep Verma | ||
| DOI : 10.14445/22312803/IJCTT-V23P123 |
Gagandeep Kaur, Dr. Amandeep Verma "A Novel Approach for Classification of Indoor Scenes". International Journal of Computer Trends and Technology (IJCTT) V23(3):108-112, May 2015. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract -
Scene classification is recently growing area of
research in computer vision. A variety of approaches has been
proposed for scene classification. The literature addresses the
issues involved in indoor scene classification. The segmentation
based approaches suffer from poor performance of segmentation
and object-based approaches involve series of complex tasks like
segmentation, training a large number of classifier and
recognition. In this study, a novel approach for classification of
indoor scenes into multiple classes has been proposed. The
proposed feature representation is entirely based on extracting
structural properties of the scene images. The proposed method
uses Gaussian filter in pre-processing phase to reduce noise from
image followed by using morphological operations to extract
edge features from image. The one-vs-all Support Vector
Machines (SVM) learning model is employed for learning and
classification. To test the performance of classification system, a
database of five indoor classes i.e. bedroom, living room, dining
room, office and kitchen has been taken from MIT-indoor
dataset. The images have been taken under different under
different illumination conditions and different viewpoints. The
accuracy of 84% and sensitivity of 56% has been obtained for
five indoor classes.
References
[1]C.-F. Tsai, “Bag-of-Words Representation in Image
Annotation: A Review,” ISRN Artif. Intell., vol. 2012, pp. 1–
19, 2012.
[2]J. Vogel and B. Schiele, “Semantic Modeling of Natural
Scenes for Content-Based Image Retrieval,” Int. J. Comput.
Vis., vol. 72, no. 2, pp. 133–157, Jul. 2006.
[3]A. Oliva, W. Hospital, and L. Ave, “Modeling the Shape of
the Scene : A Holistic Representation of the Spatial
Envelope ,” Int. J. Comput. Vis., vol. 42, no. 3, pp. 145–175,
2001.
[4]S. Lazebnik, C. Schmid, and J. Ponce, “Beyond Bags of
Features: Spatial Pyramid Matching for Recognizing Natural
Scene Categories,” IEEE Comput. Soc. Conf. Comput. Vis.
Pattern Recognit. - Vol. 2 (CVPR’06)E, vol. 2, pp. 2169–2178,
2006.
[5]A. Quattoni, U. C. B. Eecs, and A. Torralba, “Recognizing
Indoor Scenes,” IEEE CS Conf. Comput. Vis. Pattern
Recognit., 2009.
[6]S. Gupta, P. Arbelaez, and J. Malik, “Perceptual
Organization and Recognition of Indoor Scenes from RGB-D
Images,” 2013 IEEE Conf. Comput. Vis. Pattern Recognit., pp.
564–571, Jun. 2013.
[7]P. Espinace, T. Kollar, N. Roy, and a. Soto, “Indoor scene
recognition by a mobile robot through adaptive object
detection,” Rob. Auton. Syst., vol. 61, no. 9, pp. 932–947, Sep.
2013.
[8]J. Wu and J. M. Rehg, “CENTRIST: A Visual Descriptor
for Scene Categorization.,” IEEE Trans. Pattern Anal. Mach.
Intell., vol. 33, no. 8, pp. 1489–1501, Dec. 2010.
[9]L. Antanas, M. Hoffmann, P. Frasconi, T. Tuytelaars, and
L. De Raedt, “A relational kernel-based approach to scene
classification,” 2013 IEEE Work. Appl. Comput. Vis., pp.
133–139, Jan. 2013.
[10]C. S. Chan, “A fuzzy qualitative approach for scene
classification,” 2012 IEEE Int. Conf. Fuzzy Syst., vol. 1, pp.
1–8, Jun. 2012.
[11]G. Csurka, C. R. Dance, L. Fan, J. Willamowski, C. Bray,
and D. Maupertuis, “Visual Categorization with Bags of
Keypoints,” Work. Stat. Learn. Comput. Vision, Eur. Conf.
Comput. Vis., vol. 1, 2004.
[12]D. Gokalp and S. Aksoy, “Scene Classification Using
Bag-of-Regions Representations,” 2007 IEEE Conf. Comput.
Vis. Pattern Recognit., pp. 1–8, Jun. 2007.
[13]D. G. Lowe, “Distinctive Image Features from Scale-
Invariant Keypoints,” Int. J. Comput. Vis., vol. 60, no. 2, pp.
91–110, Nov. 2004.
[14]H. Madokoro, a. Yamanashi, and K. Sato, “Unsupervised
semantic indoor scene classification for robot vision based on
context of features using Gist and HSV-SIFT,” Pattern
Recognit. Phys., vol. 1, no. 1, pp. 93–103, Aug. 2013.
[15]A. Swadzba and S. Wachsmuth, “Indoor Scene
Classification using combined 3D and Gist Features,” Comput.
Vision–ACCV , Springer Berlin Heidelberg, Springer Berlin
Heidelberg, vol. 6493, pp. 201–215, 2011.
[16]J. Wu and J. M. Rehg, “CENTRIST: A visual descriptor
for scene categorization,” IEEE Trans. Pattern Anal. Mach.
Intell., vol. 33, no. 8, pp. 1489–1501, 2011.
[17]L. Xie, J. Wang, B. Guo, B. Zhang, and Q. Tian,
“Orientational Pyramid Matching for Recognizing Indoor
Scenes,” 2014 IEEE Conf. Comput. Vis. Pattern Recognit., pp.
3734–3741, Jun. 2014.
[18]L. Nan, K. Xie, and A. Sharf, “A search-classify approach
for cluttered indoor scene understanding,” ACM Trans.
Graph., vol. 31, no. 6, p. 1, Nov. 2012.
[19]L. J. Li, H. Su, Y. Lim, and L. Fei-Fei, “Object bank: An
object-level image representation for high-level visual
recognition,” Int. J. Comput. Vis., vol. 107, no. 1, pp. 20–39,
2014.
[20]Ariadna Quattoni and Antoni Torralba, “Indoor scene
database,” 2009. [Online]. Available:
http://web.mit.edu/torralba/www/indoor.html.
[21]R. C. W. Gonzalez, Digital Image Processing. Pearsons
Eucation India, 3rd ed., 2009.
[22]A. Pronobis, B. Caputo, P. Jensfelt, and H. I. Christensen,
“A realistic benchmark for visual indoor place recognition,”
Rob. Auton. Syst., vol. 58, no. 1, pp. 81–96, Jan. 2010.
Keywords
Indoor Scene Classification, Structural properties,
Morphological Gradient.