Content-Based Image Retrieval using SIFT for binary and greyscale images

This paper presents an alternative approach for Content Based Image Retrieval (CBIR) using Scale Invariant Feature Transform (SIFT) algorithm for binary and gray scale images. The motivation to use SIFT algorithm for CBIR is due to the fact that SIFT is invariant to scale, rotation and translation as well as partially invariant to affine distortion and illumination changes. Inspired by these facts, this paper investigates the fundamental properties of SIFT for robust CBIR by using MPEG-7, COIL-20 and ZuBuD image databases. Our approach uses detected keypoints and its descriptors to match between the query images and images from the database. Our experimental results show that the proposed CBIR using SIFT algorithm producing excellent retrieval result for images with many corners as compared to retrieving image with less corners.

[1]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[2]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[3]  Ankita Sharma,et al.  Radial Basis Function used in CBIR for SIFT Features , 2012 .

[4]  Alex Pentland,et al.  Photobook: Content-based manipulation of image databases , 1996, International Journal of Computer Vision.

[5]  Lei Zhang,et al.  A CBIR method based on color-spatial feature , 1999, Proceedings of IEEE. IEEE Region 10 Conference. TENCON 99. 'Multimedia Technology for Asia-Pacific Information Infrastructure' (Cat. No.99CH37030).

[6]  Mario A. Nascimento,et al.  A compact and efficient image retrieval approach based on border/interior pixel classification , 2002, CIKM '02.

[7]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[8]  Dragutin Petkovic,et al.  Query by Image and Video Content: The QBIC System , 1995, Computer.

[9]  Toshikazu Kato,et al.  Query by Visual Example - Content based Image Retrieval , 1992, EDBT.

[10]  Luciano da Fontoura Costa,et al.  Effective image retrieval by shape saliences , 2003, 16th Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI 2003).

[11]  Rong Jin,et al.  Content-based image retrieval: An application to tattoo images , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[12]  Ari Visa,et al.  Binary Histogram in Image Classification for Retrieval Purposes , 2003, WSCG.

[13]  Fatos T. Yarman-Vural,et al.  BAS: a perceptual shape descriptor based on the beam angle statistics , 2003, Pattern Recognit. Lett..

[14]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[15]  Jun Qin,et al.  A SVM face recognition method based on Gabor-featured key points , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[16]  Mario A. Nascimento,et al.  Color-based image retrieval using binary signatures , 2002, SAC '02.

[17]  Jan J. Koenderink,et al.  What is a "Feature"? , 1993 .