Abstract
An important step in content-based image retrieval is finding an interesting object within an image. We propose a method for extracting an interesting object from a complex background. Interesting objects are generally located near the center of the image and contain regions with significant color distribution. The significant color is the more frequently co-occurred color near the center of the image than at the background of the image. A core object region is selected as a region a lot of pixels of which have the significant color, and then it is grown by iteratively merging its neighbor regions and ignoring background regions. The final merging result called a central object may include different color-characterized regions and/or two or more connected objects of interest. The central objects automatically extracted with our method matched well with significant objects chosen manually.
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References
Vailaya, A., Figueiredo, M.A.T., Jain, A.K., and Zhang, H.J.: Image Classification for Content-Based Indexing. IEEE Trans. on Image Processing. 10 (1) (2001) 117–130
Eakins, J.P.: Towards Intelligent Image Retrieval. Pattern Recognition. 35 (2002) 3–14
Carson, C., Thomas, M., Belongie, S., Hellerstein, J.M., and Malik, J.: Blobworld: A System for Region-Based Image Indexing and Retrieval. VISUAL’99. Amsterdam, Netherlands, (1999) 509–516
Kam, A.H., Ng, T.T., Kingsbury, N.G., and Fitzgerald, W.J.: Content Based Image Retrieval through Object Extraction and Querying. IEEE Workshop on Content-based Access of Image and Video Libraries. (2000) 91–95
Wang, W., Song, Y., and Zhang, A.: Semantics Retrieval by Region Saliency. Int’l Conf. on Image and Video Retrieval. (2002) 29–37
Osberger, W. and Maeder, A.J.: Automatic Identification of Perceptually Important Regions in an Image. IEEE Int’l Conf. on Pattern Recognition. (1998) 701–704
Lu, Y. and Guo H.: Background Removal in Image Indexing and Retrieval. Int’l Conf. on Image Analysis and Processing. (1999) 933–938
Huang, Q., Dom, B., Steels, D., Ashely, J., and Niblack, W.: Foreground/Background Segmentation of Color Images by Integration of Multiple Cues. Int’l Conf. on Image Processing. 1 (1995) 246–249
Serra, J.R. and Subirana, J.B.: Texture Frame Curves and Regions of Attention Using Adaptive Non-cartesian Networks. Pattern Recognition. 32 (1999) 503–515
Tamaki, T., Yamamura, T., and Ohnishi, N.: Image Segmentation and Object Extraction Based on Geometric Features of Regions. SPIE Conf. on VCIP’99, 3653 (1999) 937–945
Huang, J., Kumar, S.R., Mitra, M., Zhu, W.J., and Zabih, R.: Image Indexing Using Color Correlograms. Proc. Computer Vision and Pattern Recognition. (1997) 762–768
Deng, Y., Manjunath, B.S., and Shin, H.: Color Image Segmentation. IEEE Conf. on Computer Vision and Pattern Recognition. 2 (1999) 446–451
Park, C., Kim, S., Kim, J., and Kim, M.: Color Image Segmentation for Content Based Image Retrieval Using a Modified Color Histogram Intersection Technique. Int’l Conf. on Multimedia Technology and Its Applications. (2003) 146–151
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Kim, S., Park, S., Kim, M. (2003). Central Object Extraction for Object-Based Image Retrieval. In: Bakker, E.M., Lew, M.S., Huang, T.S., Sebe, N., Zhou, X.S. (eds) Image and Video Retrieval. CIVR 2003. Lecture Notes in Computer Science, vol 2728. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45113-7_5
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DOI: https://doi.org/10.1007/3-540-45113-7_5
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