Abstract
Owing the widespread use of digital image, methods of high efficiency of image retrieval from WWW are becoming urgent requirements to users. But the traditional search engines are mostly based on keywords. This paper presents a modular image search engine based on keywords and contents, which organically combines search engine technology of keywords and images’ color feature. The system searches images from WWW by WEB robots, extracts their relevant contents and color features, and then stores them into a database. When a user gives a query, the system displays the results according to the user’s search requirements. For the color features of an image, a quantified method based on the maximum pixels ratio of irregular connected regions is raised. Experiments show that the method improves the retrieval efficiency and can get an expected search result more accurately, so as to satisfy the customer’s needs.
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References
Wang, J., Liu, X., Liu, X.: Image Retrieval Method Based On Color Features and Relevant Feedback. Learned Journal of Shandong University of Technology (Natural Science) 23(3), 58–62 (2009)
Wang, W., Wang, Z.: Extraction Method Based On Content of Colorful Image Color Features. Computer Aided Design and Graphics Technology 13(6), 565–569 (2008)
Huang, Y., Guo, L.: An Image Retrieval Based on Object Region’s Color and Texture Features. Journal of Nanjing University of Science and Technology 27(3), 286–289 (2009)
Fan, Y., Wang, R.S.: Color Image Segmentation Content Based Image Retrieval. Journal of Computer Research and Development 39(3), 376–381 (2008)
Bongiovanni, G.: Image Segmentation by A Multi-resolution Approach. Pattern Recognition 26(12), 18–27 (2009)
Malik, J., Belongie, F., Leugn, T.: Contour and Texture Analysis for Image Segmentation. Journal of Computer Vision 43(1), 7–27 (2011)
Eom, K.B.: Segmentation of Monochrome and Color Textures Using Moving Average Modeling Approach. Image and Vision Computing 17(3), 233–244 (2006)
Ning, Y.-D.: A Region Growing Algorithm Based on Color and Space Information. Computer Knowledge and Technology 5(12), 3196–3198 (2010)
Li, W., Huang, H.: The Application of Seeded Region Growing Technique in Color Image Segmentation. Micro Computer System 29(6), 1163–1167 (2010)
Chen, W.-B.: The performance comparison of some image similarity-matching methods. Journal of Computer Application 30(1), 98–100 (2011)
Zitova, B., Flusser, J.: Image registration methods: A survey. Image and Vision Computing 21(11), 977–1000 (2008)
Wang, X., Yang, H., Zheng, H., Wu, J.: Image Retrieval Algorithm Based On Block Color Histogram of Visual Weight. Automatic Technology, 1–4 (2008)
Fliekner, et al.: Query by image and video content: the QBIC system. IEEE Computer 28(9), 23–32 (2006)
Yang, Z., Peng, Y., Xiao, J.: Visual Vocabulary Optimization with Spatial Context for Image Annotation and Classification. In: Schoeffmann, K., Merialdo, B., Hauptmann, A.G., Ngo, C.-W., Andreopoulos, Y., Breiteneder, C. (eds.) MMM 2012. LNCS, vol. 7131, pp. 89–102. Springer, Heidelberg (2012)
Popescu, B., Iancu, A., Dan Burdescu, D., Brezovan, M., Ganea, E.: Evaluation of Image Segmentation Algorithms from the Perspective of Salient Region Detection. In: Blanc-Talon, J., Kleihorst, R., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2011. LNCS, vol. 6915, pp. 183–194. Springer, Heidelberg (2011)
Yang, Y., Zi, H., Shen, H., Zhou, X.: Mining multi-tag association for image tagging. LNCS, vol. 14(2), pp. 133–156 (2011)
Torjmen, M., Pinel-Sauvagnat, K., Boughanem, M.: Using Pseudo-Relevance Feedback to Improve Image Retrieval Results. In: Peters, C., Jijkoun, V., Mandl, T., Müller, H., Oard, D.W., Peñas, A., Petras, V., Santos, D. (eds.) CLEF 2007. LNCS, vol. 5152, pp. 665–673. Springer, Heidelberg (2008)
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Huang, X., Chen, W. (2012). A Modular Image Search Engine Based on Key Words and Color Features. In: Pan, Z., Cheok, A.D., Müller, W., Chang, M., Zhang, M. (eds) Transactions on Edutainment VIII. Lecture Notes in Computer Science, vol 7220. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31439-1_18
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DOI: https://doi.org/10.1007/978-3-642-31439-1_18
Publisher Name: Springer, Berlin, Heidelberg
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