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Content-based image retrieval is the implementation of computer vision techniques to image retrieval problem, that is, issue of looking for images taking from high-end cameras in large image dataset. It aims to finding pictures of interest from an extensive picture database using the visual content of the pictures. “Content-based” implies that the hunt will break down the genuine content of the picture instead of the metadata, for example, tags, descriptions, and keywords linked with the picture. The term “content” in this context may point to shapes, hues, surfaces, or some other data that can be derived from the picture itself. Graphical processing unit is helpful in most picture handling applications because of multithread execution of algorithms, programmability and minimal effort. The substantial quantities of pictures have increased challenges to computer to store and oversee information adequately and productively. Features can be extracted parallel from the pictures with graphical processing unit utilizing different procedures. Utilizing Graphical processing unit based feature extraction algorithm can perform feature extraction easily and in fast and efficient way. The NVIDIA CUDA is fundamentally new computing architecture technology that enables the graphical processing unit to solve difficult, time taking, complex problems. This paper aims to find out the similarity between images that is query image and image present in dataset. The paper aims at the performance of various content-based image retrieval algorithms, which include, local binary pattern and rotated local binary pattern. An improvement has been made in Rotated local binary pattern where mapping function has been incorporated so as to give better results, with the help of mapping we are able to sort the higher and lower priority pixel values of query image and images present in our database.
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Picard, D., Revel, A., & Cord, M. (2012). An application of swarm intelligence to distributed image retrieval. Information Sciences: An International Journal, 192, 71–81. CrossRef
Ke, Y., Tang, X., & Jing, F. (2006). The design of high-level features for photo quality assessment. In Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 1, Washington, DC, USA, June 17–22, 2006, pp. 419–426.
Redi, J. A., Hobfeld, T., Korshunov, P., Mazza, F., Povoa I., & Keimel, C. (2013). Crowdsourcing-based multimedia subjective evaluations: A case study on image recognizability and aesthetic appeal. In Proceedings of the 2nd ACM International Workshop on Crowdsourcing for Multimedia, Barcelona, Spain, October 22–22, 2013, pp. 29–34.
Datta, R., Joshi, D., Li, J., & Wang, J. Z. (2006). Studying aesthetics in photographic images using a computational approach. In Proceedings of the 9th European conference on Computer Vision. Graz, Volume Part III, Austria, May 07–13, 2006, pp. 288–301.
Zhang, X., & Chen, X. (2016). Robust sketch-based image retrieval by saliency detection. In Proceedings of the 22nd International Conference on MultiMedia Modeling, Vol. 9516, Miami, FL, USA, January 04–06, 2016, pp. 515–526.
Mehta, R., & Egiazarian, K. (2016). Dominant rotated local binary patterns (DRLBP) for texture classification. Pattern Recognition Letters, 71, 16–22. CrossRef
Isola, P., Parikh, D., Torralba, A., & Oliva, A. (2011). Understanding the intrinsic memorability of images. In Proceedings of the 24th International Conference on Neural Information Processing Systems, Granada, Spain, December 12–15, 2011, pp. 2429–2437.
Mazza, F., Silva, M. P., Callet, P., & Heynderickx, I. (2015). What do you think of my picture? Investigating factors of influence in profile images context perception. In Proceedings of Human Vision and Electronic Imaging XX, San Francisco, United States, March 2015.
Isola, P. Xiao, J., Torralba, A., & Oliva, A. (2011). What makes an image memorable? In Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition, Washington, DC, USA, June 20–25, 2011, pp. 145–152.
Mazza, F., Da Silva, M. P. & Le Callet, P. (2014). Would you hire me? Selfie portrait images perception in a recruitment context. In Proceedings of Human Vision and Electronic Imaging XIX, Vol. 9014, San Francisco, California, United States, February 02, 2014, pp. 90140X–90140X.
Mazza, F., Silva, M. P, & Callet, P. (2015). Think again about my picture: Different approaches investigating factors of influence in profile images context perception. In Proceedings of Sino-French Workshop on Information and Communication Technologies, Nantes, France, January, 2015.
- Improved Rotated Local Binary Pattern
D. R. Gangodkar
- Springer Singapore