Image Segmentation Algorithm Based on Spatial Pyramid and Visual Salience
An image segmentation algorithm based on Spatial Pyramid and visual salience is proposed in the paper. The segmentation algorithm is divided into five steps. The first step is extracting the global features of images to be processed. The second step is dividing the image into some sub-blocks according to different scales. And the third step is extracting the sub-block features of different scales and connecting the features sequentially. The fourth step is calculating the salience of different sub-blocks. The last step is segmenting the salient objects from the source image. The segmentation algorithm detects salient parts of image by means of both color histogram and spatial pyramid. The significance of pixels can be calculated by means of color and pattern. The algorithm assigns different weights to different pixels and sub-blocks. According to experiment results, the segmentation algorithm proposed in the paper outperforms other segmentation in precision, recall and time complexity.
KeywordsImage segmentation Spatial pyramid Visual salience Similarity Feature fusion
- 1.Ali, N.M., Jun, S.W., Karis, M.S., Ghazaly, M.M., Mohd, M.S.A.: Object classification and recognition using Bag-of-Words (BoW) model. In: 2016 IEEE 12th International Colloquium on Signal Processing and its Applications, CSPA 2016, pp. 216–220 (2016)Google Scholar
- 3.Kim, H.U., Lee, D.Y., Sim, J.Y., Kim, C.S.: SOWP: spatially ordered and weighted patch descriptor for visual tracking. In: 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, pp. 3011–3019 (2015)Google Scholar
- 4.Li, G., Xie, Y., Lin, L., Yu, Y.: Instance-level salient object segmentation. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, 2017, pp. 247–256 (2017)Google Scholar
- 5.Kavitha, C., Rao, B.P., Govardhan, A.: Image retrieval based on color and texture features of the image sub-blocks. Int. J. Comput. Appl. 15(7), 33–37 (2011)Google Scholar
- 8.Jiang, X., Feng, K., Lin, H., Tang, J., Zhou, Z., Li, J.: Active contours driven by local gaussian distribution fitting and local robust statistics. J. Inf. Hiding Multimed. Signal Process. 9(1), 89–98 (2018)Google Scholar