Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast wavelet histogram techniques for image indexing
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A survey of content-based image retrieval with high-level semantics
Pattern Recognition
Texture image retrieval using rotated wavelet filters
Pattern Recognition Letters
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
A multiscale approach to texture-based image retrieval
Pattern Analysis & Applications
Complex wavelet transform with vocabulary tree for content based image retrieval
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
The design of approximate Hilbert transform pairs of wavelet bases
IEEE Transactions on Signal Processing
Texture image retrieval using new rotated complex wavelet filters
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Texture classification using rotated wavelet filters
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A new, fast, and efficient image codec based on set partitioning in hierarchical trees
IEEE Transactions on Circuits and Systems for Video Technology
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In this paper, we propose a new approach for image retrieval by using texture features. Texture features are grabbed by using the combinations of two-dimensional complex wavelet transform (CWT) and rotated complex wavelet filters (RCWF) in combination with spatial orientation tree (SOT) and vocabulary tree (VT). The parent-offspring relationship among the wavelet coefficients in multi-resolution wavelet sub-bands are demonstrated with the help of SOT which gives the set of descriptor vectors for each image that are further indexed by the use of vocabulary tree. The directional information has been captured precisely with the help of CWT and RCWT as when compared with discrete wavelet transform. The proposed method is well established on Texture database and significant improvement in average recall rate is seen as compared to the method adopted using complex wavelet transform and rotated complex wavelet filter.