Abstract
Detection of the visual salient image area for image segmentation, image recognition, and adaptive compression application is beneficial. It makes an object, a person, or some pixels stand out against the background of the image and provide support for image recognition and target detection. The detection can simplify the process of computer visual image processing and improve the effect and efficiency of computer visual inspection. This paper introduces a kind of salient detection method, without any manual intervention, and uses the method of decomposing brightness, color space, negative map solution, and standard deviation to find the super-distance pixel in the image. The method of clustering is used to separate the region of objects and image background, and output RGB color salient objects image. Moreover, it can accurately highlight the object contour and internal pixels. This method studies the characteristics of the original pixels such as brightness or color and utilizes the image basis features to achieve the image saliency detection. It has high adaptive detection ability, low time complexity and high computational efficiency.
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Wang, Z., Jin, J., Liu, Z. (2019). Standard Deviation Clustering Combined with Visual Psychological Test Algorithm for Image Segmentation. In: Li, J., Wang, S., Qin, S., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2019. Lecture Notes in Computer Science(), vol 11888. Springer, Cham. https://doi.org/10.1007/978-3-030-35231-8_37
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