Abstract
Abstract. From the last decade, the multi-scale image segmentation is getting a particular interest and practically being used for object-based image analysis. In this study, we have addressed the issues on multi-scale image segmentation, especially, in improving the performances for validity of merging and variety of derived region’s shape. Firstly, we have introduced constraints on the application of spectral criterion which could suppress excessive merging between dissimilar regions. Secondly, we have extended the evaluation for smoothness criterion by modifying the definition on the extent of the object, which was brought for controlling the shape’s diversity. Thirdly, we have developed new shape criterion called aspect ratio. This criterion helps to improve the reproducibility on the shape of object to be matched to the actual objectives of interest. This criterion provides constraint on the aspect ratio in the bounding box of object by keeping properties controlled with conventional shape criteria. These improvements and extensions lead to more accurate, flexible, and diverse segmentation results according to the shape characteristics of the target of interest. Furthermore, we also investigated a technique for quantitative and automatic parameterization in multi-scale image segmentation. This approach is achieved by comparing segmentation result with training area specified in advance by considering the maximization of the average area in derived objects or satisfying the evaluation index called F-measure. Thus, it has been possible to automate the parameterization that suited the objectives especially in the view point of shape’s reproducibility.
Highlights
The image segmentation technique is regarded as a very important and efficient elemental technology which is utilized for detection, classification or recognition of objectives included in images
We can confirm that the size of scale parameter (SP) does not affect directly on the average size of derived objects, and remarkable effects on introducing aspect ratio proposed in this study to reconstitute the given shape of targeted buildings
We have introduced the constraint for spectral evaluation so as not to make the improper merging of adjacent regions
Summary
The image segmentation technique is regarded as a very important and efficient elemental technology which is utilized for detection, classification or recognition of objectives included in images. We could realize to obtain more accurate, flexible and diverse image segmentation results which reflected the shape characteristics of the objectives. We demonstrated that these improvements and extensions contributed to improve the performance of the reproducibility on the shape of objectives in both of the qualitative and quantitative evaluations. The proposed approach is achieved by comparing training area, which are marked by evaluator in advance, with the derived objects which include training area This can realize the calculation of an optimum parameter in the state of maximizing the resulting average area size of objects and/or F-measure over a predefined threshold.
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