Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

This is an Open Access publication published under CSC-OpenAccess Policy.
Publications from CSC-OpenAccess Library are being accessed from over 74 countries worldwide.
An Approach For Single Object Detection In Images
Kartik Umesh Sharma, Nileshsingh V. Thakur
Pages - 278 - 293     |    Revised - 10-08-2014     |    Published - 15-09-2014
Volume - 8   Issue - 5    |    Publication Date - September / October 2014  Table of Contents
Object Detection, Steiner Tree, Object Classification.
This paper discusses an approach for object detection and classification. Object detection approaches find the object or objects of the real world present either in a digital image or a video, where the object can belong to any class of objects. Humans can detect the objects present in an image or video quite easily but it is not so easy to do the same by machine, for this, it is necessary to make the machine more intelligent. Presented approach is an attempt to detect the object and classify the same detected object to the matching class by using the concept of Steiner tree. A Steiner tree is a tree in a distance graph which spans a given subset of vertices (Steiner Points) with the minimal total distance on its edges. For a given graph G, a Steiner tree is a connected and acyclic sub graph of G. This problem is called as Steiner tree problem where the aim is to find a Steiner minimum tree in the given graph G. Basically the process of object detection can be divided as object recognition and object classification. A Multi Scale Boosted Detector is used in the presented approach, which is the combination of multiple single scale detectors; in order to detect the object present in the image. Presented approach makes use of the concept of Steiner tree in order to classify the objects that are present in an image. To know the class of the detected object, the Steiner tree based classifier is used. In order to reach to the class of the object, four parameters namely, Area, Eccentricity, Euler Number and Orientation of the object present in the image are evaluated and these parameters form a graph keeping each parameter on independent level of graph. This graph is explored to find the minimum Steiner tree by calculating the nearest neighbor distance. Experimentations are carried out using the standard LabelMe dataset. Obtained results are evaluated based on the performance evaluation parameters such as precision and recall.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
1 M. Everingham, L. Van Gool, C. K. I.Williams, J.Winn, and A. Zisserman. The PASCAL Visual Object Classes Challenge Results, 2007-2009.
2 M. R. Garey and D. S. Johnson. Computers and Intractability: A guide to the theory of NPcompleteness.Freeman, San Francisco, 1978.
3 M. Charikar, C. Chekuri, T. Cheung, Z. Dai, A. Goel, and S. Guha. Approximation algorithms for directed Steiner problems. In Symposium on Discrete Algorithms, 1998.
4 L. Zosin and S. Khuller. On directed Steiner trees. In Symposium on Discrete Algorithms,2002.
5 S. Segvic, Z. Kalafati´c, and I. Kova?cek, ‘Sliding window object detection without spatial clustering of raw detection responses’, Proceedings of the Computer Vision Winter Workshop, 2011.
6 B. Subburaman, Venkatesh and S. Marcel, ‘Fast Bounding Box Estimation based Face Detection’, ECCV, Workshop on Face Detection, 2010.
7 F. Comaschi, S. Stuijk, T. Basten, and H. Corporaal, ‘RASW: a Run Time Adaptive Sliding Window to Improve Viola-Jones Object Detection’, IEEE Transactions, 2012.
8 P. Viola and M. J. Jones. Robust real-time face detection. IJCV, Vol: 57, No: 2, 2004.
9 X. Yang, H. Liu, and L.J. Latecki, ‘Contour Based Object Detection as Dominant Set Computation’, Journal on Pattern Recognition, Vol.45 No.5, pp.1927-1936, 2012.
10 K. Amine and M.H. Farida, ‘An Active Contour for Range Image Segmentation’, Signal & Image Processing: An International Journal (SIPIJ) Vol.3, No.3, 2012.
11 J. Shotton, A. Blake and R. Cipolla, ‘Multi Scale Categorical Object Recognition Using Contour Fragments’, IEEE Transactions of Pattern Analysis and Machine Intelligence,Vol.30 No.7, pp.1270-1281, 2008.
12 P.F.Felzenszwalb and D.P.Huttenlocher, ‘Efficient Graph Based Image Segmentation’,International Journal of Computer Vision, Vol.59, No.2, pp.167 – 181, 2004.
13 P. Dasigi and C.V. Jawahar, ‘Efficient Graph Based Image Matching for Recognition and Retrieval’, Proceedings of National Conference on Computer Vision, Pattern recognition,2008.
14 C. Gunduz-Demir, M. Kandemir, A.B. Tosun and C. Sokmensuer, ‘Automatic Segmentation of Colon Glands using Object-Graphs’, Medical Image Analysis, Vol. 14, pp.1-12, 2010.
15 J. Kim, M. Kim, S. Lee, J. Oh, S. Oh and H. Yoo, ‘Real-Time Object Recognition with Neuro-Fuzzy Controlled Workload-Aware Task Pipelining, Micro, IEEE, Vol. 29, No.6, pp.28-43, 2009.
16 N.V. Lopes, P. Couto, A. Jurio and P. Melo-Pinto, ‘Hierarchical Fuzzy Logic Based Approach for Object Tracking’, Knowledge-Based Systems, Vol.54, pp. 255-268, 2013.
17 T.C. Rajakumar, S.A. Perumal and N. Krishnan, ‘A Fuzzy Filtering Model for Contour Detection’, ICTACT Journal on Soft Computing, Vol.01, No.04, 2011.
18 R. Perko and A. Leonardis, ‘A framework for visual-context-aware object detection in still images’, Computer Vision and Image Understanding, Vol.114, pp. 700-711, 2010.
19 B. Peralta, P. Espinace and A. Soto, ‘Adaptive Hierarchical Contexts for object recognition with conditional mixture of trees’, Proceedings British Machine Vision Conference, pp.121.1-121.11, 2012.
20 C. Galleguillos and S. Belongie, ‘Context Based Object Categorization: A critical survey’,Computer Vision and Image Understanding, 2010.
21 A. Torrent, X. Lladó, J. Freixenet and A. Torralba, ‘A Boosting Approach for the Simultaneous Detection and Segmentation of Generic Objects’, Journal of Pattern Recognition Letters, Vol: 34, No: 13, pp. 1490-1498, 2013.
22 R. Hussin, M.R. Juhari, N.W. Kang, R.C. Ismail and A. Kamarudin, “Digital Image Processing Techniques for Object Detection from Complex Background Image”. In International Symposium on Robotics and Intelligent Sensors, Volume: 41, pp. 340-344,2012.
23 I. Laptev, ‘Improving Object Detection with Boosted Histograms’, Journal of Vision Computing, Vol: 27, No: 5, pp. 535-544, 2009.
24 E. Susanne, Hambrusch and L. TeWinkel, “Parallel Heuristics for Determining Steiner Trees in Images”. In Computer Science Technical Reports, Report No. 90-1033, 1990.
25 C. Lin, S. Chen, C. Li, Y. Chang and C. Yang, “Obstacle-Avoiding Rectilinear Steiner Tree Construction Based on Spanning Graphs”. In IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Vol. 27, Issue. 4, pp. 643-653, 2008.
26 L.E. Liu and C. Sechen, “Multilayer Chip-Level Global Routing Using an Efficient GraphBased Steiner Tree Heuristic”. In IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Vol. 18, Issue. 10, pp. 1442-1451, 1999.
27 K.U. Sharma and N.V. Thakur, “A Review and an Approach for Object Detection in Images”, International Journal of Computational Vision and Robotics, Inderscience Publisher, 2014. (Submitted).
Mr. Kartik Umesh Sharma
Department of PG Studies (Computer Science and Engineering) Prof Ram Meghe Collge of Engineering and Management Badnera-Amravati. 444701, INDIA - India
Mr. Nileshsingh V. Thakur
Department of PG Studies (Computer Science and Engineering) Prof Ram Meghe Collge of Engineering and Management Badnera-Amravati. 444701, INDIA - India