TOP 10 STORAGE & RETRIEVAL PAPERS : RECOMMENDED READING – SIGNAL & IMAGE PROCESSING

1. TOP 10 STORAGE &
RETRIEVAL PAPERS :
RECOMMENDED READING –
SIGNAL & IMAGE
PROCESSING
http://airccse.org/top10/Storage_retrieval.html

2. Citation Count – 174
CONTENT BASED IMAGE RETRIEVAL USING COLOR AND
TEXTURE
Manimala Singha and K.Hemachandran
Dept. of Computer Science, Assam University, Silchar India. Pin code 788011
ABSTRACT
The increased need of content based image retrieval technique can be found in a number of
different domains such as Data Mining, Education, Medical Imaging, Crime Prevention, Weather
forecasting, Remote Sensing and Management of Earth Resources. This paper presents the
content based image retrieval, using features like texture and color, called WBCHIR (Wavelet
Based Color Histogram Image Retrieval).The texture and color features are extracted through
wavelet transformation and color histogram and the combination of these features is robust to
scaling and translation of objects in an image. The proposed system has demonstrated a
promising and faster retrieval method on a WANG image database containing 1000 general-
purpose color images. The performance has been evaluated by comparing with the existing
systems in the literature.
KEYWORDS
Image Retrieval, Color Histogram, Color Spaces, Quantization, Similarity Matching, Haar
Wavelet, Precision and Recall.
For More Details : http://aircconline.com/sipij/V3N1/3112sipij04.pdf
Volume Link : http://www.airccse.org/journal/sipij/vol3.html
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Shape features using Image and its complement”, 15th International Conference on Advance
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8. Citation Count – 75
ALGORITHM AND TECHNIQUE ON VARIOUS EDGE
DETECTION: A SURVEY
Rashmi , Mukesh Kumar and Rohini Saxena
Department of Electronics and Communication Engineering,
SHIATS- Allahabad, UP.-India
ABSTRACT
An edge may be defined as a set of connected pixels that forms a boundary between two disjoints
regions. Edge detection is basically, a method of segmenting an image into regions of
discontinuity. Edge detection plays an important role in digital image processing and practical
aspects of our life. .In this paper we studied various edge detection techniques as Prewitt, Robert,
Sobel, Marr Hildrith and Canny operators. On comparing them we can see that canny edge
detector performs better than all other edge detectors on various aspects such as it is adaptive in
nature, performs better for noisy image, gives sharp edges , low probability of detecting false
edges etc.
KEYWORDS
Edges, Edge detection, Canny edge detection.
For More Details : http://aircconline.com/sipij/V4N3/4313sipij06.pdf
Volume Link : http://www.airccse.org/journal/sipij/vol4.html
References
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[8] Xiaogbin Wang, Baokui Li, Qingbo Geng , “Runway Detection and Tracking for Unmanned
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IEEE,Proceedings of the 8th World Congress on Intelligent Control and Automation July 6-
9 2010, Jinan, China, 978-1- 4244-6712-9/10/$26.00 ©2010 IEEE
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Application in License Plate Recognition, IEEE TRANSATION 2010, 20978-1-4244-5392-
4/10/.

10. Citation Count – 18
FOLIAGE PLANT RETRIEVAL USING POLAR FOURIER
TRANSFORM, COLOR MOMENTS AND VEIN FEATURES
Abdul Kadir , Lukito Edi Nugroho , Adhi Susanto and Paulus Insap Santosa
Department of Electrical Engineering,
Gadjah Mada University, Yogyakarta, Indonesia
ABSTRACT
This paper proposed a method that combines Polar Fourier Transform, color moments, and vein
features to retrieve leaf images based on a leaf image. The method is very useful to help people in
recognizing foliage plants. Foliage plants are plants that have various colors and unique patterns
in the leaf. Therefore, the colors and its patterns are information that should be counted on in the
processing of plant identification. To compare the performance of retrieving system to other
result, the experiments used Flavia dataset, which is very popular in recognizing plants. The
result shows that the method gave better performance than PNN, SVM, and Fourier Transform.
The method was also tested using foliage plants with various colors. The accuracy was 90.80%
for 50 kinds of plants.
KEYWORDS
Color Moments, Plant Retrieval, PFT (Polar Fourier Transform), PNN, SVM, Vein features
For More Details : http://aircconline.com/sipij/V2N3/2311sipij01.pdf
Volume Link : http://www.airccse.org/journal/sipij/vol2.html
References
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Features”. Advances in Computational Sciences and Technology , 3 (3), pp. 387-396.

13. Citation Count – 14
A REVIEW ON FEATURE EXTRACTION TECHNIQUES IN FACE
RECOGNITION
Rahimeh Rouhi1
, Mehran Amiri2
and Behzad Irannejad3
1,2
Department of Computer Engineering, Islamic Azad University, Science and
Research Branch, Kerman, Iran
3
Department of Computer Engineering, Islamic Azad University, Kerman, Iran
ABSTRACT
Face recognition systems due to their significant application in the security scopes, have been of
great importance in recent years. The existence of an exact balance between the computing cost,
robustness and their ability for face recognition is an important characteristic for such systems.
Besides, trying to design the systems performing under different conditions (e.g. illumination,
variation of pose, different expression and etc. ) is a challenging problem in the feature extraction
of the face recognition. As feature extraction is an important step in the face recognition
operation, in the present study four techniques of feature extraction in the face recognition were
reviewed, subsequently comparable results were presented, and then the advantages and the
disadvantages of these methods were discussed.
KEYWORDS
Face Recognition Systems &Feature Extraction
For More Details : http://aircconline.com/sipij/V3N6/3612sipij01.pdf
Volume Link : http://www.airccse.org/journal/sipij/vol3.html
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[20] V.Perlibakas, (2006) "Face recognition using Principal Component Analysis and Log-Gabor
filter", Image processing Analysis Laboratory, Computational Technologies center.
[21] T. BARBU, V. BARBU, V. BIGA and D. COCA, (2009) "A PDE variational approach to
image denoising and restoration", Nonlinear Analysis: Real World Applications, 10, 3, pp.
1351–1361.
[22] T. ACHARYA, A. K. RAY, (2005) "Image Processing – Principles and Applications",
Wiley Inter Science.
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with classification based on feature distributions", Pattern Recognition 29.

16. Citation Count – 12
EFFICIENT IMAGE RETRIEVAL USING REGION BASED IMAGE
RETRIEVAL
Niket Amoda and Ramesh K Kulkarni
Department of Electronics and Telecommunication Engineering, Vivekanand
Institute of Technology, University of Mumbai M.G. Road Fort, Mumbai, India
ABSTRACT
Early image retrieval techniques were based on textual annotation of images. Manual annotation
of images is a burdensome and expensive work for a huge image database. It is often
introspective, context-sensitive and crude. Content based image retrieval, is implemented using
the optical constituents of an image such as shape, colour, spatial layout, and texture to exhibit
and index the image. The Region Based Image Retrieval (RBIR) system uses the Discrete
Wavelet Transform (DWT) and a k-means clustering algorithm to segment an image into regions.
Each region of the image is represented by a set of optical characteristics and the likeness
between regions and is measured using a particular metric function on such characteristics.
KEYWORDS
Content based image retrieval, K-Means Algorithm, Discrete Wavelet Transform, Region Based
Image Retrieval.
For More Details : http://aircconline.com/sipij/V4N3/4313sipij02.pdf
Volume Link : http://www.airccse.org/journal/sipij/vol4.html
References
[1] D.Lowe, “Object recognition from local scale-invariant features,” in ICCV, 1999, pp. 1150–
1157.
[2] Y.J.Zhang “A survey on evaluation methods for image segmentation”, Pattern Recognition
29 (8) (1996) 1335 - 1340
[3] A.Jain, “Data clustering: 50 years beyond k-means,” Pattern Recognition Letters, vol. 31,
no. 8, pp. 651 – 666, June 2010.
[4] W.Zhao, H.Ma, Q.He, "Parallel K-Means Clustering Based on MapReduce," in: Cloud
Computing, vol. 5931, pp. 674-679, 2009.

17. [5] W.D.Arthur, S. Vassilvitskii, “K-means++: the Advantages of careful seeding,” in Proc.
2007 Symposium on Discrete Algorithms, pp.1027-1035.
[6] Rafael C. Gonzalez, Richard E. Woods, " Digital Image Processing" , Second Edition,
Prentice Hall Upper Saddle River, New Jersey 07458, TA1632.G66 2001, 698-740
[7] Fast Multiresolution Image Querying, International Conference on Computer Graphics and
Interactive Techniques, 1995: Charles E.Jacobs, Adam Finkelstein, David H. Salesin

18. Citation Count – 12
FACE DETECTION AND RECOGNITION USING BACK
PROPAGATION NEURAL NETWORK AND FOURIER GABOR
FILTERS
Anissa Bouzalmat , Naouar Belghini, Arsalane Zarghili and Jamal Kharroubi
Department of Computer Sciences,
Sidi Mohamed Ben Abdellah University, Fez, Morocco
ABSTRACT
Face recognition is a field of computer vision that uses faces to identify or verify a person. In this
paper, we present a neural network system for face recognition. Feature vector based on Fourier
Gabor filters are used as input of the Back Propagation Neural Network (BPNN). To extract the
features vector of the whole face in image, we use an algorithm for detecting skin human faces in
color images and then we introduce Gabor filters with 8 different orientations and 5 different
resolutions to get maximum information. Experiments show that the proposed method yields
results.
KEYWORDS
Face Detection, Face Recognition, Bilinear Interpolation, Fourier Transform, Gabor Filter, Neural
Network
For More Details : http://aircconline.com/sipij/V2N3/2311sipij02.pdf
Volume Link : http://www.airccse.org/journal/sipij/vol2.html
References
[1] K. Sandeep, A.N. Rajagopalan,”Human Face Detection in Cluttered Color Images Using
Skin Color and Edge Information” ,ICVGIP Proceeding, 2002.
[2] H. Deng, L. Jin, L. Zhen, and J. Huang. A new facial expression recognition method based
on local gabor filter bank and pca plus lda. International Journal of Information Technology,
11(11):86-96, 2005.
[3] L. Shen and L. Bai. Information theory for gabor feature selection for face recognition.
Hindawi Publishing Corporation, EURASIP Journal on Applied Signal Processing, Article
ID 30274, 2006.

19. [4] J Essam Al Daoud, ”Enhancement of the Face Recognition Using a Modified Fourier-Gabor
Filter”,Int. J. Advance. Soft Comput. Appl., Vol. 1, No. 2, 2009.
[5] Z. Y. Mei, Z. Ming, and G. YuCong. Face recognition based on low diamensional gabor
feature using direct fractional-step lda. In Proceedings of the Computer Graphics, Image and
Vision: New Treds (CGIV'05), IEEE Computer Society,2005.
[6] B. Schiele, J. Crowley, ”Recognition without correspondence using mul-tidimensional
receptive field histograms”,International Journal on Com-puter Vision.36:3152,2000.
[7] Christopher M Bishop, “Neural Networks for Pattern Recognition” London, U.K.:Oxford
University Press, 1995.
[8] H. Martin Hunke, Locating and tracking of human faces with neural network, Master’s
thesis,University of Karlsruhe, 1994.
[9] Henry A. Rowley, Shumeet Baluja, and Takeo Kanade. “Neural network based face
detection,”IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(I), pp.23-38,
1998.
[10] B. Schiele and J. Crowley. “Recognition without correspondence using multidimensional
receptive field histograms”. International Journal on Computer Vision, 36:3152, 2000.
[11] K Messer, J Matas, J Kittler, J Luettin, and G maitre, ” Xm2vtsdb: The extended m2vts
database”, In Second International Conference of Audio and Video-based Biometric Person
Authentication, March 1999.

20. Citation Count – 10
FACE RECOGNITION APPROACH BASED ON WAVELET -
CURVELET TECHNIQUE
Muzhir Shaban Al-Ani and Alaa Sulaiman Al-waisy
Department of Computer Science, College of Computer, Al-Anbar University, Iraq
ABSTRACT
In this paper, a novel face recognition approach based on wavelet-curvelet technique, is proposed.
This algorithm based on the similarities embedded in the images, That utilize the wavelet-curvelet
technique to extract facial features. The implemented technique can overcome on the other
mathematical image analysis approaches. This approaches may suffered from the potential for a
high dimensional feature space, Therefore it aims to reduce the dimensionality that reduce the
required computational power and memory size. Then the Nearest Mean Classifier (NMC) is
adopted to recognize different faces. In this work, three major experiments were done. two face
databases (MAFD & ORL, and higher recognition rate is obtained by the implementation of this
techniques.
KEYWORDS
Face recognition, wavelet transform, curvelet transform, Nearest Mean Classifier.
For More Details : http://aircconline.com/sipij/V3N2/3212sipij02.pdf
Volume Link : http://www.airccse.org/journal/sipij/vol3.html
References
[1] Mohammad Shahin Mahanta, " Linear Feature Extraction with Emphasis on Face
Recognition" , Graduate Department of Electrical and Computer Engineering University of
Toronto, Copyright © 2009
[2] Dr. Salah M. Rahal, Dr. Hatim A. Abu Samah and et al," Secure Identification System –
SIS", College of Computer & Information Sciences, King Saud University – 2006.
[3] Sarat C. Dass Anil K. Jain, " Fingerprint-Based Recognition", September 20, 2006.
[4] Yusuf Atilgan, " FACE RECOGNITION", MAY, 2009.
[5] Rabia Jafri* and Hamid R. Arabnia*," A Survey of Face Recognition Techniques", Journal
of Information Processing Systems, Vol.5, No.2, June 2009.

21. [6] Anil K. Jain, Arun Ross and Salil Prabhakar, " An Introduction to Biometric Recognition",
IEEE Transactions On Circuits And Systems For Video Technology, VOL. 14, NO. 1,
JANUARY 2004.
[7] Gerhard X. Ritter and Joseph N. Wilson, " Handbook of Computer Vision Algorithms in
Image Algebra", ISBN: 0849326362 Pub Date: 05/01/96.
[8] Ali Karami *, Bahman Zanj and Azadeh Kiani Sarkaleh, " Persian sign language (PSL)
recognition using wavelet transform and neural networks", Faculty of Engineering,
University of Guilan, P.O. Box 41635-3756, Rasht, Iran, journal homepage:
www.elsevier.com/locate/eswa, 3 September 2010.
[9] Zhao Lihong, Song Ying, Zhu Yushi, Zhang Cheng, Zhang Xili, " Face Recognition Based
on Image Transformation", 978-0-7695-3571-5/09 $25.00 © 2009 IEEE .
[10] Aunss Sinan Maki, " Hand Palm Recognition Using Wavelet Transform", Al-Nahrain
University College of Science, 2004.
[11] Harin Sellahewa and Sabah A. Jassim, "Image-Quality-Based Adaptive Face Recognition",
Ieee Transactions On Instrumentation And Measurement, Vol. 59, NO. 4, APRIL 2010.
[12] SHREEJA R and SHALINI BHATIA, " Facial Feature Extraction Using Statistical
Quantities Of Curve Coefficients", International Journal of Engineering Science and
Technology Vol. 2(10), 2010.
[13] Rowan Seymour, Darryl Stewart and JiMing, " Comparison of Image Transform-Based
Features for Visual Speech Recognition in Clean and Corrupted Videos", EURASIP Journal
on Image and Video Processing ,Volume 2008.
[14] Ishrat Jahan Sumana, " Image Retrieval Using Discrete Curvelet Transform", Monash
University, Australia, November, 2008.
[15] Jianhong Xie, " Face Recognition Based on Curvelet Transform and LS-SVM", Proceedings
of the 2009 International Symposium on Information Processing (ISIP’09), Huangshan, P.
R. China, August 21-23, 2009, pp. 140-143.
[16] Ming Li and Fuwen Wu，Xueyan Liu, " Face Recognition Based on WT, FastICA and RBF
Neural Network", Third International Conference on Natural Computation 0-7695-2875-
9/07 $25.00 © 2007 IEEE.
[17] Yu Su, Shiguang Shan, Xilin Chen and Wen Gao, " Hierarchical Ensemble of Global and
Local Classifiers for Face Recognition", IEEE Transactions On Image Processing, VOL. 18,
NO. 8, AUGUST 2009.
[18] Niu Liping, Li XinYuan and Dou Yuqiang, " Bayesian Face Recognition Using Wavelet
Transform", 978-0-7695-3752-8/09 $25.00 © 2009 IEEE.

22. [19] Mohammed Rziza, Mohamed El Aroussi, Mohammed El Hassouni, Sanaa Ghouzali and
Driss Aboutajdine," Local Curvelet Based Classification Using Linear Discriminant
Analysis for Face Recognition", International Journal of Computer Science 4:1 2009.
[20] Dinesh KUMAR, Shakti KUMAR and C. S. RAI, " Feature selection for face recognition: a
memetic algorithmic approach", Journal of Zhejiang University SCIENCE, June 10, 2009.

23. Citation Count – 9
IMAGE RETRIEVAL AND RE-RANKING TECHNIQUES - A
SURVEY
Mayuri D. Joshi, Revati M. Deshmukh, Kalashree N.Hemke, Ashwini Bhake and
Rakhi Wajgi
Computer Technology Department, Yeshwantrao Chavan College of Engineering,
Nagpur, Maharashtra, India
ABSTRACT
There is a huge amount of research work focusing on the searching, retrieval and re-ranking of
images in the image database. The diverse and scattered work in this domain needs to be
collected and organized for easy and quick reference. Relating to the above context, this paper
gives a brief overview of various image retrieval and re-ranking techniques. Starting with the
introduction to existing system the paper proceeds through the core architecture of image
harvesting and retrieval system to the different Re-ranking techniques. These techniques are
discussed in terms of approaches, methodologies and findings and are listed in tabular form for
quick review.
KEYWORDS
Image Retrieval, Re-ranking, MI learning, Ontology, Multi-latent vector.
For More Details : http://aircconline.com/sipij/V5N2/5214sipij01.pdf
Volume Link : http://www.airccse.org/journal/sipij/vol5.html
References
[1] Venkat N.Gudivada, Vijay V. Raghavan "Content-Based Image Retrieval Systems" IEEE
Transaction 0018-9162, 1995 .
[2] Edward Remias, Gholamhosein Sheikholeslami, Aidong Zhang." Block-Oriented Image
Decomposition and Retrieval in Image Database Systems". IEEE Transaction 0-8186-7469-
5, 1996.
[3] Soo-Chang Pei, Senior Member, IEEE, and Ching-Min Cheng." Extracting Color Features
and Dynamic Matching for Image Data-Base Retrieval". IEEE Transactions On circuits and
systems for video technology, VOL. 9, NO. 3, APRIL 1999.

24. [4] Yang Hu, Nenghai Yu, Zhiwei Li, Mingjing Li. "Image Search Result Clustering And Re-
ranking via PARTIAL GROUPING". IEEE transaction ,1-4244-1017-7/07, 2007.
[5] Szabolcs Sergy´an, Budapest Tech, John von Neumann ,Faculty of Informatics." Color
Histogram Features Based Image Classification in Content-Based Image Retrieval
Systems".6th International IEEE Symposium on Applied Machine Intelligence and
Informatics-2008.
[6] Yihun Alemu, Jong-bin Koh, Muhammed Ikram, Dong-Kyoo Kim." Image Retrieval in
Multimedia Databases: A Survey". Fifth International Conference on Intelligent Information
Hiding and Multimedia Signal Processing ,IEEE-2009.
[7] Jie Xia, Yun Fu, Yijuan Lu, Qi Tian." REFINING IMAGE RETRIEVAL USING ONE-
CLASS CLASSIFICATION". IEEE Transaction 978-1-4244-4291-1,2009
[8] Jes´us M. Almendros-Jim´enez ,Jos´e A. Piedra and Manuel Cant´on." AN ONTOLOGY-
BASED MODELING OF AN OCEAN SATELLITE IMAGE RETRIEVAL
SYSTEM".IEEE transaction 978- 1-4244-9566-5 ,2010.
[9] Xinmei Tian, Dacheng Tao, Member, IEEE, Xian-Sheng Hua, Member, IEEE, and Xiuqing
Wu." Active Re-ranking for Web Image Search". IEEE TRANSACTIONS ON IMAGE
PROCESSING, VOL. 19, NO. 3, MARCH 2010.
[10] K.A. Shaheer Abubacker, L.K. Indumathi." Attribute Associated Image Retrieval and
Similarity Reranking". Proceedings of the International Conference on Communication and
Computational Intelligence – 2010, Kongu Engineering College, Perundurai, Erode,
T.N.,India.27 – 29 December,2010.pp.235-240.
[11] Vidit Jain, Manik Varma." Learning to Re-Rank: Query-Dependent Image Re-Ranking
Using Click Data". ACM 978-1-4503-0632-4,April 2011.
[12] Lixin Duan, Wen Li, Ivor Wai-Hung Tsang, and Dong Xu, Member, IEEE. "Improving Web
Image Search by Bag-Based Re-ranking".IEEE TRANSACTIONS ON IMAGE
PROCESSING, VOL. 20, NO. 11, NOVEMBER 2011.
[13] Linjun Yang, Member, IEEE, and Alan Hanjalic, Senior Member, IEEE." Prototype-Based
Image Search Re-ranking".IEEE TRANSACTIONS ON MULTIMEDIA, VOL. 14, NO. 3,
JUNE 2012.

25. Citation Count – 7
IMAGE RETRIEVAL SYSTEM BY USING CWT AND SUPPORT
VECTOR MACHINES
Sanchita Pange and Sunita Lokhande
Sinhgad college of Engineering Vadgaon, Pune, India
ABSTRACT
This paper presents an image retrieval system based on dual tree complex wavelet transform
(CWT) and support vector machines (SVM). There are two attributes of image retrieval system.
First, images that a user needs through query image are similar to a group of images with the
same conception. Second, there exists non-linear relationship between feature vectors of different
images. Standard DWT (Discrete Wavelet Transform), being non-redundant, is a very powerful
tool for many non-stationary Signal Processing applications, but it suffers from three major
limitations; 1) shift sensitivity, 2) poor directionality, and 3) absence of phase information. To
reduce these limitations, Complex Wavelet Transform (CWT). The initial motivation behind the
development of CWT was to avail explicitly both magnitude and phase information. At the first
level, for low level feature extraction, the dual tree complex wavelet transform will be used for
both texture and color-based features. At the second level, to extract semantic concepts, we will
group medical images with the use of one against all support vector machines. We are used here
Euclidean distance for to measure the similarity between database features and query features.
Also we can use a correlation-based distance metric for comparison of SVM distances vectors.
The proposed approach has superior retrieval performance over the existing linear feature
combining techniques
KEYWORDS
Content Based Image Retrieval system, Dual Tree complex Wavelet Transform, Support Vector
Machines
For More Details : http://aircconline.com/sipij/V3N3/3312sipij06.pdf
Volume Link : http://www.airccse.org/journal/sipij/vol3.html
References
[1] M.NARAYANA, “Comparison between Euclidean Distance Metric and SVM for CBIR
using Level Set Features”, ISSN: 0975-5462 Vol. 4 No.01 January 2012
[2] Vanitha.L. and Venmathi.A.R,”Classification of Medical Images Using Support Vector
Machine” IPCSIT vol.4 (2011) © (2011)

26. [3] S. Nandagopalan, Dr. B. S. Adiga, and N. Deepak “ A Universal Model for Content-Based
Image Retrieval” World Academy of Science, Engineering and Technology 46 2008)
[4] Anurag Sahajpal, Terje Kristensen,” Transcription of Text by Incremental Support Vector
Machine”IEEE International Symposium on Intelligent Control Munich, Germany, October
4-6, 2006
[5] J.-H. HAN, D.-S.HUANG, T.M. LOK, M. R. LYU, A Novel Image Retrieval System Based
On BP Neural Network. International Joint Conference on Neural Networks (IJCNN 2005),
[6] M. KOKARE, P. K. BISWAS, B. N. CHATTERJI, Texture Image Retrieval Using New
Rotated Complex Wavelet Filters. SMC-B, 35(6) (2005), 1168–1178.
[7] P. JANNEY, G. SRIDHAR, V. SRIDHAR, Enhancing Capabilities of Texture Extraction
for Color Image Retrieval. In Proceedings of World Enformatika Conference (Turkey),
(2005).
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Color Image Retrieval. WEC, 5 (2005), 282–285.
[9] S. DEB, Y. ZHANG, An Overview of Content-based Image Retrieval Techniques. (2004)
[10] Dengsheng Zhang and Guojun Lu,” similarity of measurement for image retrieval”, IEEE
2003
[11] R. C. VELTKAMP, M. TANASE, Content-based Image Retrieval Systems: A Survey. UU-
CS-2000- 34, Department of Computer Science, Utretch University, October 2002.
[12] J. A. K. SUYKENS, T. VAN GESTEL, J. DE BRABANTER B. DE MOOR,
J.VANDEWALLE, Least Squares Support Vector Machines. World Scientific, Singapore,
2002.
[13] Avi Kak and Christina Pavlopoulou,’’ Content-Based Image Retrieval from Large Medical
Databases” IEEE proceedings of the First International Symposium on 3D Data Processing
Visualization and Transmission 2002
[14] R. PETER, N. KINGSBURY, Complex Wavelets Features for Fast Texture Image retrieval.
Proc IEEE Int. Conf. on Image Processing, (1999), 25–28.
[15] V. VAPNIK, Statistical Learning Theory. Wiley, New York, 1998.
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Mining and Knowledge Discovery, 2(2) (1998), 955–974.

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[21] Alexandros Karatzoglou, David Meyer, Kurt Hornik,” Support Vector Machines in R”
[22] Chih-Wei Hsu, Chih-Chung Chang, and Chih-Jen Lin,”A Practical Guide to Support Vector
Classification”
[23] Panu Erasto,”Support Vector Machines -Backgrounds and Practice”
[24] Yang Liu, Rui Wang, Yingsheng Zeng and Hangen He, “An Improvement of One-against-
All Method for Multiclass Support Vector Machine”
[25] Yi Liu and Yuan F. Zheng,”One-Against-All Multi-Class SVM Classification Using
Reliability Measures”

28. Citation Count – 7
IMAGE INFORMATION RETRIEVAL FROM INCOMPLETE
QUERIES USING COLOR AND SHAPE FEATURES
Bikesh Kumar Singh1
, A. S. Thoke2
, Keshri Verma3
and Ankita Chandrakar4
1
Department of Biomedical Engineering, N. I.T Raipur, C.G (India)
2,4
Department of Electrical Engineering, N. I.T Raipur, C.G (India)
3
Department of M.C.A, N. I.T Raipur, C.G (India)
ABSTRACT
Content based image retrieval (CBIR) is the task of searching digital images from a large
database based on the extraction of features, such as color, texture and shape of the image. Most
of the research in CBIR has been carried out with complete queries which were present in the
database. This paper investigates utility of CBIR techniques for retrieval of incomplete and
distorted queries. Studies were made in two categories of the query: first is complete and second
is incomplete. The query image is considered to be distorted or incomplete image if it has some
missing information, some undesirable objects, blurring, noise due to disturbance at the time of
image acquisition etc. Color (hue, saturation and value (HSV) color space model) and shape
(moment invariants and Fourier descriptor) features are used to represent the image. The
algorithm was tested on database consisting of 1875 images. The results show that retrieval
accuracy of incomplete queries is highly increased by fusing color and shape features giving
precision of 79.87%. MATLAB ® 7.01 and its image processing toolbox have been used to
implement the algorithm.
KEYWORDS
Content based image retrieval, color image, incomplete query image, color feature, shape feature.
For More Details : http://aircconline.com/sipij/V2N4/2411sipij18.pdf
Volume Link : http://www.airccse.org/journal/sipij/vol2.html
References
[1] Christian Wolf, Jean-Michel Jolion, Walter Kropatsch , Horst Bischof (2000), “Content
based Image Retrieval using Interest Points and Texture Features, Proceedings of IEEE,
International conference on pattern recongnition,pp 234 - 237 vol.4.
[2] Jianlin Zhang, Wensheng Zou(2010),”Content-Based Image Retrieval Using Color and Edge
Direction Features”, Proceedings of IEEE, International conference on Advanced Computer
Control, pp- 459 – 462.

29. [3] B. S. Manjunath(2001), “Color and texture descriptors”, IEEE Transactions, on Circuits and
Systems for Video Technology, 11(6): 703–715.
[4] Ji-quan ma, “Content-Based Image Retrieval with HSV Color Space and Texture
Features”(2009), Proceedings of IEEE, International conference onWeb Information System
and Mining,pp-61-63.
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feedback in MARS” , Proceedings of the IEEE, International Conference on Image
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& Spectral Features”, Proceedings of National Conference Technologia 2010, MPCCET
Bhilai.
[7] Tomislav Petkovi´c,Josip Krapac(2002),” Tehnical Report Shape description with Fourier
descriptors”, journal of documentation ,collected from web http://www.google.co.in.
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Paulo M. A. Marques (2009), Content-Based Retrieval of Medical Images: from Context to
Perception Proceedings of IEEE, 22nd international conference on Computer based medical
system , pp 1-8.
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[16] http://www.vision.caltech.edu/Image_Datasets/Caltech101.

30. [17] Yimo Tao,Shih Chung B.Lo , Mathew T. Freedman, and Jianhua Xaun(2007), “ A
premilimary study of Content based mammographic masses retrieval” , proceedings of
SPIE, Conference on Medical Imaging : Computer-Aided Diagnosis.