Content Based Image Retrieval Using Sketches
This paper aims to introduce the problems and challenges concerned with the design and creation of CBIR systems, which is based on a free hand sketch (Sketched based image retrieval-SBIR). This analysis led us to studying the usability of a method for computing dissimilarity between user-produced pictorial queries and database images according to features extracted from Gray-Level Co-occurrence Matrix (GLCM) automatically.
CBIR is generally characterized by the methods that consumes less time. Hence fast content – based image retrieval is a need of the day especially image mining for shapes, as image database is growing exponentially in size with time. In this paper, texture features extracted from GLCM, tested, and investigated on different standard databases is proposed, it exhibits invariant to rotation. The retrieval performance of the proposed method is showed for both the dinosaurs retrieval efficiency achieved about 95% and precision also 95% where color is not dominant. It is also observed that the proposed method achieved low retrieval performance over these four image features for sketch based and color dominant images. This process can be used as coarse level in hierarchical CBIR that reduces the database size from very large set to a small one. This tiny database can further be scrutinized rigorously using the Edge Histogram Descriptor (EHD) and Color and Color Co-occurrence Matrix (CCM) etc.
Unable to display preview. Download preview PDF.
- 3.Györök, G.: Embedded hybrid controller with programmable analog circuit. In: IEEE 14th International Conference on Intelligent Systems, pp. 59.1–59.4 (May 2010)Google Scholar
- 4.Hu, R., Barnard, M., Collomosse, J.: Gradient _eld descriptor for sketch based image retrieval and localization. In: International Conference on Image Processing, pp. 1–4 (2010)Google Scholar
- 5.Jain, A.K., Lee, J.E., Jin, R.: Sketch to photo matching: a feature-based approach. In: Proc. SPIE, Biometric Technology for Human Identification VII, vol. 7667, pp. 766702–766702 (2010)Google Scholar
- 6.Jain, A.K., Lee, J.E., Jin, R., Gregg, N.: Graffiti-ID: matching retrieval of graffiti images. In: ACM MM, MiFor 2009, pp. 1–6 (2009)Google Scholar
- 7.Jain, A.K., Lee, J.E., Jin, R., Gregg, N.: Content based image retrieval: an application to tattoo images. In: IEEE International Conference on Image Processing, pp. 2745–2748 (November 2009)Google Scholar
- 8.Liu, Y., Dellaert, F.: A classification based similarity metric for 3D image retrieval. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 800–805 (June 1998)Google Scholar
- 9.Lowe, D.G.: Object Recognition from Local Scale-Invariant Features. In: IEEE International Conference on Computer Vision, vol. 2, p. 1150 (1999)Google Scholar
- 10.Smith, J.R., Chang, S.F.: VisualSEEK: a fully automated content based image query system. In: ACM Multimedia 1996, pp. 97–98 (1996)Google Scholar
- 11.Szántó, B., et al.: Sketch4Match–Content-based Image Retrieval System Using Sketches. In: 9th IEEE International Symposium on Applied Machine Intelligence and Informatics, January 27-29 (2011)Google Scholar
- 13.Narayana, M., Sandeep, V.M., Kulkarni, S.: Skeletal Distance Mapped Functional Features for Improved CBIR. International Congress for Global Science and Technology (ICGST)-GVIP Journal 11(3), 47–56 (2011)Google Scholar