International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 11, No. 1, 8 Jan. 2019
Cover page and Table of Contents: PDF (size: 1151KB)
Efficient Image Retrieval through Hybrid Feature Set and Neural Network
Full Text (PDF, 1151KB), PP.44-53
Views: 0 Downloads: 0
Author(s)
Nitin Arora
1,2,*
Alaknanda Ashok
3
Shamik Tiwari
2
Index Terms
Content-based Image Retrieval, Information Retrieval, Color features, Texture features, Shape features
Abstract
Images are an important part of daily life. Any person cannot easily control the huge repository of digitally existing images. Extensive scanning of the image database is very much essential to search a particular image from the huge repository. In some cases, this procedure becomes very exhaustive also. As a result, if a count of ten thousand, lakhs or considerably more images are included in the image database, then it may be transformed into a tedious and never-ending process. Content-based image retrieval (CBIR) is a technique, which is used for retrieving an image. This type of image retrieval procedure is centered on the real content of the image. This paper proposed a model of the hybrid feature set of Haar wavelets and Gabor features and analyzed with different existing models image retrieval. Content-based image retrieval using hybrid feature set of Haar wavelets and Gabor features superiors on other models.
Cite This Paper
Nitin Arora, Alaknanda Ashok, Shamik Tiwari, "Efficient Image Retrieval through Hybrid Feature Set and Neural Network", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.11, No.1, pp. 44-53, 2019. DOI: 10.5815/ijigsp.2019.01.05
Reference
[1]Pattanaik S and Bhalke D 2012 Beginners to Content-Based Image Retrieval. International Journal of Science, Engineering and Technology Research. 1: 40–44
[2]Chen Y, Wang J, and Krovetz R 2005 CLUE: Cluster- based retrieval of images by unsupervised learning. IEEE Transaction Image Process. 141: 187– 1201
[3]Wang J, Li J, and Wiederholdy G 2000 SIMPLIcity: Semantics-sensitive integrated matching for picture libraries. Lecture Notes Computer. Science. 1929: 360–371
[4]Hany F , Attiya G, and El-Fishawy N 2013 Comparative Study on CBIR based on Color Feature. International Journal of Computer Applications. 78 : 975–8887
[5]Hall E 1971 A Survey of Preprocessing and Feature Extraction Techniques for Radiographic Images. IEEE Transaction Computer. C-20: 1032–1044
[6]Rangari F and Ramarao U 2013 Searching by Content based image retrieval through combined features. 1: 85–93
[7]Afifi Ahmed J and Ashour Wesam M Image Retrieval Based on Content using Color Feature. ISRN Comput. Graph. 341: 560–564.
[8]Jhanwar N, Chaudhuri S, Seetharaman G and Zavidovique B 2004 Content based image retrieval using motif co-occurrence matrix. Image and vision Computing Journal. 22: 1211–1220
[9]Huang P and Dai SK 2003 Image retrieval by texture similarity. Pattern Recognition. 36: 665– 679
[10]Lin CH, Chen RT and Chan YK 2009 A smart content-based image retrieval system based on color and texture feature. Image and vision Computing Journal. 27 : 658–665
[11]Rao M, Rao B, and Govardhan A 2011 CTDCIRS: Content based Image Retrieval System based on Dominant Color and Texture Features. International Journal of Computer Applications. 18 : 40–46
[12]Hiremath P and Pujari J 2007 Content Based Image Retrieval based on Color, Texture and Shape features using Image and its complement. International Journal of Computer Science Security. 1 : 25–35
[13]Li J, Wang J and Wiederhold G 2000 IRM: integrated region matching for image retrieval. Proceedings ACM International Conference on Multimedia. pp. 147–156
[14]Zhang D 2004 Improving Image Retrieval Performance by Using Both Color and Texture Features. Third International Conference Image and Graphics. pp. 172–175
[15]Sharma N, Rawat P, and Singh J 2011 Efficient CBIR Using Color Histogram Processing. Signal Image Processing: An International Journal. 2: 94–112
[16]Kushwah V and Agrawal A 2014 Study on Query Based Clustering Technique for Content Based Image Retrieval. International Journal of Research in Engineering Advance Technology. 2: 1–6
[17]Singha M and Hemachandran K 2012 Content Based Image Retrieval using Color and Texture. Signal and Image Processing: An International Journal. 3: 39–57
[18]An Y, Riaz M, and Park J 2010 CBIR based on adaptive segmentation of HSV color space. 12th International Conference on Computer Modeling Simulation. pp. 248–251
[19]Chakravarti R and Meng X 2009 A Study of Color Histogram Based Image Retrieval. Sixth International Conference on information Technology-New Generation., pp. 1323–1328
[20]Wei C, Li Y, Chau W, and Li C 2009 Trademark image retrieval using synthetic features for describing global shape and interior structure. Pattern Recognition. 42: 386–394.
[21]Smith J and Chang S 1996 VisualSEEk: A Fully Automated Content-based Image Query System. Proceedings fourth ACM International Conference on Multimedia. pp. 87–98
[22]Li J and Wang J 2003 automatic linguistic indexing of pictures by a statistical modeling approach. IEEE Transaction Pattern Analysis and Machine Intelligence. 25:1075– 108
[23]Tiwari, S., Prasad Shukla, V., Biradar, S. R., & Singh, A. K. (2014). Blind restoration of motion blurred barcode images using ridgelet transform and radial basis function neural network. ELCVIA: electronic letters on computer vision and image analysis, 13(3), 63-80.
[24]Tiwari, S., Shukla, V. P., Biradar, S. R., & Singh, A. K. (2014). Blur parameters identification for simultaneous defocus and motion blur. CSI transactions on ICT, 2(1), 11-22.
[25]Kane, L. and Khanna, P., 2017. Vision-based mid-air unistroke character input using polar signatures. IEEE Transactions on Human-Machine Systems, 47(6), pp.1077-1088.