SIFT Feature with Relevance Feedback for Image Retrieval

In this paper, we used the Scale Invariant Feature Transform (SIFT) feature for image retrieval. SIFT descriptors are invariant to image scaling, transformation, rotation and partially invariant to illumination changes and affine, gives the local features of an image. Therefore, feature from the images can be extracted more accurately by using SIFT than color, texture, shape and spatial relations. SIFT descriptor vectors for each image is indexed by making the use of vocabulary tree. Further, relevance feedback technique is used to bridge the gap between low level features and high level concepts. The proposed method is tested on mixed database of Corel and Caltech 3000 images which shows a significant improvement in precision and average recall rate. KeywordsSIFT, Image retrieval, Relevance feedback, Vocabulary tree.

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

[2]  Anil Balaji Gonde,et al.  A new feature for image retrieval using a'trous wavelet transform and textons , 2010, Int. J. Comput. Vis. Robotics.

[3]  Thomas S. Huang,et al.  Content-based image retrieval with relevance feedback in MARS , 1997, Proceedings of International Conference on Image Processing.

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

[5]  Bo Zhang,et al.  Support vector machine learning for image retrieval , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[6]  R. P. Maheshwari,et al.  Multiscale Ridgelet Transform for content based image retrieval , 2010, 2010 IEEE 2nd International Advance Computing Conference (IACC).

[7]  Cordelia Schmid,et al.  A performance evaluation of local descriptors , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Tony Lindeberg,et al.  Feature Detection with Automatic Scale Selection , 1998, International Journal of Computer Vision.

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

[10]  Rudra Prakash Maheshwari,et al.  Content-Based Image Retrieval using colour feature and colour bit planes , 2010 .

[11]  Ying Liu,et al.  A survey of content-based image retrieval with high-level semantics , 2007, Pattern Recognit..

[12]  Cordelia Schmid,et al.  A Comparison of Affine Region Detectors , 2005, International Journal of Computer Vision.

[13]  Cordelia Schmid,et al.  Indexing Based on Scale Invariant Interest Points , 2001, ICCV.

[14]  Thomas S. Huang,et al.  Relevance feedback: a power tool for interactive content-based image retrieval , 1998, IEEE Trans. Circuits Syst. Video Technol..

[15]  David Nistér,et al.  Scalable Recognition with a Vocabulary Tree , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[16]  Anil K. Jain,et al.  Image retrieval using color and shape , 1996, Pattern Recognit..

[17]  Licheng Jiao,et al.  Combining color and texture features for image retrieval , 2007, International Symposium on Multispectral Image Processing and Pattern Recognition.

[18]  Kebin Jia,et al.  A Novel Image Retrieval Algorithm Based on ROI by Using SIFT Feature Matching , 2008, 2008 International Conference on MultiMedia and Information Technology.

[19]  Cordelia Schmid,et al.  An Affine Invariant Interest Point Detector , 2002, ECCV.