Abstract
Sketch Based Image Retrieval (SBIR) is one of the efficient way of image mining. By considering growth in multimedia technologies, demand of image retrieval increased nowadays. CBIR work on shape, color, texture like properties of an image, where as SBIR works on query by sketch input. This paper presents survey about different image retrieval techniques and data retrieval methods. This paper includes various approaches for image and data retrieval from an image.
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References
Wang S, Zhang J, Han TX, Miao Z (2015) Sketch based image retrieval through hypothesis-driven object boundary selection with HLR descriptor. IEEE Trans Multimedia 17(7)
Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell PAMI 8(6):679–698
Bozas K, Izquierdo E (2012) Large scale sketch based image retrieval using patch hashing. Adv Visual Comput 7431:210219
Eitz M, Hildebrand K, Boubekeur T, Alexa M (2009) A descriptor for large scale image retrieval based on sketched feature lines. In: Proceedings of the 6th Eurographics Symposium on Sketch-based Interfaces Modeling, p 2936
Sharath Kumara YS, Gurub DS (2015) Retrieval of flower based on sketches. In: International conference on information and communication technologies. Proc Comput Sci 46:1577–1584
Liu T, Xu H. Medical image segmentation based on a hybrid region-based active contour model
Wang J, Zhao Y, Qi Q, Huo Q, Zou J, Ge C, Liao J (2018) MindCamera: interactive sketch-based image retrieval and synthesis. In: Special section on recent advantages of computer vision based on Chinese Conference on Computer Vision (CCCV) 2017, vol 6
Eitz M, Hildebrand K, Boubekeur T, Alexa M (2011) Sketch-based image retrieval: benchmark and bag-of-features descriptors. IEEE Trans Vis Comput Graphics 17(11):1624–1636
Hu R, Barnard M, Collomosse J (2010) Gradient field descriptor for sketch based retrieval and localization. In: Proceedings of the IEEE International Conference on Image Process (ICIP), September 2010, pp 1025–1028
Hu R, Collomosse J (2013) A performance evaluation of gradient field hog descriptor for sketch based image retrieval. Comput Vis Image Understand 117(7):790806
Cao Y, Wang C, Zhang L, Zhang L (2011) Edgel index for large-scalesketch-based image search. In: Proceedings of the IEEE Conference Computer Vision Pattern Recognition (CVPR), June 2011, pp 761–768
Yang C, Tiebe O, Pietsch P, Feinen C, Kelter U, Grzegorzek M (2014) Shape-based object retrieval by contour segment matching. In: Proceedings of the IEEE International Conference on Image Process (ICIP), October 2014, pp 2202–2206
Xiao C, Wang C, Zhang L, Zhang L (2015) Sketch-based image retrieval via shape words. In: Proceedings of the ACM International Conference on Multimedia Retrieval (ICMR), pp 571–574
Hou D, Zhang W, Chen K, Lin S-J, Yu N. Reversible data hiding in color image with grayscale invariance. In: Transaction on circuits and systems for video technology
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Birari, D., Hiran, D., Narawade, V. (2020). Survey on Sketch Based Image and Data Retrieval. In: Kumar, A., Mozar, S. (eds) ICCCE 2019. Lecture Notes in Electrical Engineering, vol 570. Springer, Singapore. https://doi.org/10.1007/978-981-13-8715-9_34
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DOI: https://doi.org/10.1007/978-981-13-8715-9_34
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