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http://dx.doi.org/10.6109/jkiice.2014.18.3.687

Color-Depth Combined Semantic Image Segmentation Method  

Kim, Man-Joung (School of Electrical Engineering and Computer Science, Chungbuk National University)
Kang, Hyun-Soo (School of Electrical Engineering and Computer Science, Chungbuk National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.18, no.3, 2014 , pp. 687-696 More about this Journal
Abstract
This paper presents a semantic object extraction method using user's stroke input, color, and depth information. It is supposed that a semantically meaningful object is surrounded with a few strokes from a user, and has similar depths all over the object. In the proposed method, deciding the region of interest (ROI) is based on the stroke input, and the semantically meaningful object is extracted by using color and depth information. Specifically, the proposed method consists of two steps. The first step is over-segmentation inside the ROI using color and depth information. The second step is semantically meaningful object extraction where over-segmented regions are classified into the object region and the background region according to the depth of each region. In the over-segmentation step, we propose a new marker extraction method where there are two propositions, i.e. an adaptive thresholding scheme to maximize the number of the segmented regions and an adaptive weighting scheme for color and depth components in computation of the morphological gradients that is required in the marker extraction. In the semantically meaningful object extraction, we classify over-segmented regions into the object region and the background region in order of the boundary regions to the inner regions, the average depth of each region being compared to the average depth of all regions classified into the object region. In experimental results, we demonstrate that the proposed method yields reasonable object extraction results.
Keywords
image segmentation; depth information; watershed algorithm; object detection; semantic segmentation;
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