Abstract
In practice, appropriate computer vision and image processing techniques are usually employed to obtain image visual features. Central to functional Content Based Image Retrieval (CBIR) system is effective indexing and fast searching of images based on the visual features. Effective indexing is also essential to make CBIR system scalable for large image databases and incorporation of advanced technique such as machine learning based relevance feedback (RF). However, it is extremely difficult to know the particular feature model(s) to be used to uniquely identify certain groups of images, while including many feature models can incur dimensionality problem. In this paper, Colour Moment (CM), Gabor Wavelet (GW), and Wavelet Moment (WM) are used to encode the low-level information at global and sub-global image levels. A query by feature example retrieval (QVER) was implemented to test the retrieval performance of each feature descriptor by computing average mean precision value for L1 and L2 distance measure. Taking average of the recalls, the average mean precision values of 0.6501, 0.6330 and 0.6380 were obtained for 54-dimensional CM (CM54), 48-dimensional GW (GW48) and 40-dimensional WM (WM40) respectively. The results reveal that colour descriptor computed using only the first two statistical moments at sub-global image level gave better retrieval performance than those computed at global image level, while the converse is true for texture descriptors. Hence, CM54, GW48, and WM40 are recommended for CM, GW, and WM feature models respectively.
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References
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition CVPR 2001. IEEE (2001)
Feng, H., Chua, T.-S.: A bootstrapping approach to annotating large image collection. In: Proceedings of the 5th ACM SIGMM International Workshop on Multimedia Information Retrieval. ACM (2003)
Hughes, J.F., Foley, J.D.: Computer Graphics: Principles and Practice. Pearson Education, New York (2014)
Huang, J., Kumar, S.R., Mitra, M.: Combining supervised learning with color correlograms for content-based image retrieval. In: Proceedings of the Fifth ACM International Conference on Multimedia. ACM (1997)
Huang, J., et al.: Spatial color indexing and applications. Int. J. Comput. Vis. 35(3), 245–268 (1999)
Jain, A.K.: Fundamentals of Digital Image Processing. Prentice-Hall Inc., Upper Saddle River (1989)
Mathias, E., Conci, A.: Comparing the influence of color spaces and metrics in content-based image retrieval. In: Proceedings of SIBGRAPI 1998 International Symposium on Computer Graphics, Image Processing, and Vision. IEEE (1998)
Stricker, M.A., Orengo, M.: Similarity of color images. In: International Society for Optics and Photonics Storage and Retrieval for Image and Video Databases III (1995)
Swain, M.J., Ballard, D.H.: Color indexing. Int. J. Comput. Vis. 7(1), 11–32 (1991)
Rui, Y., Huang, T.S., Chang, S.-F.: Image retrieval: current techniques, promising directions, and open issues. J. Vis. Commun. Image Representation 10(1), 39–62 (1999)
Huang, R., Dong, S., Du, M.: A semantic retrieval approach by color and spatial location of image regions. In: International Congress on Image and Signal Processing CISP 2008. IEEE (2008)
Marukatat, S.: Image annotation using label propagation algorithm. In: Proceedings of 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology ECTI-CON 2008. IEEE (2008)
Akbas, E., Vural, F.T.Y.: Automatic image annotation by ensemble of visual descriptors. In: IEEE Conference on Computer Vision and Pattern Recognition CVPR 2007. IEEE (2007)
Qi, X., Han, Y.: Incorporating multiple SVMs for automatic image annotation. Pattern Recogn. 40(2), 728–741 (2007)
Shao, W., Naghdy, G., Phung, S.L.: Automatic annotation of digital images using colour structure and edge direction. In: IEEE International Conference on Signal Processing and Communications ICSPC 2007. IEEE (2007)
Li, J., Wang, J.Z., Wiederhold, G.: IRM: integrated region matching for image retrieval. In: Proceedings of the Eighth ACM International Conference on Multimedia. ACM (2000)
Tsai, C.-F., McGarry, K., Tait, J.: CLAIRE: a modular support vector image indexing and classification system. ACM Trans. Inf. Syst. (TOIS) 24(3), 353–379 (2006)
Meskaldji, K., Boucherkha, S., Chikhi, S.: Color quantization and its impact on color histogram based image retrieval accuracy. In: First International Conference on Networked Digital Technologies. NDT 2009. IEEE (2009)
Liu, Y., et al.: A survey of content-based image retrieval with high-level semantics. Pattern Recogn. 40(1), 262–282 (2007)
Jain, R., Kasturi, R., Schunck, B.G.: Machine Vision, vol. 5. McGraw-Hill, New York (1995)
Haralick, R.M., Shanmugam, K.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 6, 610–621 (1973)
Haralick, R.M.: Statistical and structural approaches to texture. Proc. IEEE 67(5), 786–804 (1979)
Levine, M.D.: Vision in Man and Machine. McGraw-Hill College, New York (1985)
Tamura, H., Mori, S., Yamawaki, T.: Textural features corresponding to visual perception. IEEE Trans. Syst. Man Cybern. 8(6), 460–473 (1978)
Francos, J.M.: 7 Orthogonal decompositions of 2D random fields and their applications for 2D spectral estimation. Handb. Stat. 10, 207–227 (1993)
Liu, F., Picard, R.W.: Periodicity, directionality, and randomness: wold features for image modeling and retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 18(7), 722–733 (1996)
Daugman, J.G.: Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression. IEEE Trans. Acoust. Speech Sig. Process. 36(7), 1169–1179 (1988)
Ma, W.-Y., Manjunath, B.: Edge flow: a framework of boundary detection and image segmentation. In: Proceedings of 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE (1997)
Wang, J.Z., Li, J., Wiederhold, G.: SIMPLIcity: semantics-sensitive integrated matching for picture libraries. IEEE Trans. Pattern Anal. Mach. Intell. 23(9), 947–963 (2001)
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The authors wish to appreciate the Center for Research, Innovation, and Discovery (CU-CRID) of Covenant University, Ota, Nigeria for partial funding of this research.
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Adegbola, O.A., Aborisade, D.O., Popoola, S.I., Atayero, A.A. (2018). Performance Evaluation of Visual Descriptors for Image Indexing in Content Based Image Retrieval Systems. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2018. ICCSA 2018. Lecture Notes in Computer Science(), vol 10963. Springer, Cham. https://doi.org/10.1007/978-3-319-95171-3_42
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