Saliency region selection in large aerial imagery using multiscale SLIC segmentation
Samir Sahli, Daniel A. Lavigne, Yunlong Sheng · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2012
Advents in new sensing hardwares like GigE-cameras and fast growing data transmission capability create an imbalance between the amount of large scale aerial imagery and the means at disposal for treating them. Selection of saliency regions can reduce significantly the prospecting time and computation cost for the detection of objects in large scale aerial imagery. We propose a new approach using multiscale Simple Linear Iterative Clustering (SLIC) technique to compute the saliency regions. The SLIC is fast to create compact and uniform superpixels, based on the distances in both color and geometric spaces. When a salient structure of the object is over-segmented by the SLIC, a number of superpixels will follow the edges in the structure and therefore acquires irregular shapes. Thus, the superpixels deformation betrays presence of salient structures. We quantify the non-compactness of the superpixels as a salience measure, which is computed using the distance transform and the shape factor. To treat objects or object details of various sizes in an image, or the multiscale images, we compute the SLIC segmentations and the salient measures at multiple scales with a set of predetermined sizes of the superpixels. The final saliency map is a sum of the salience measures obtained at multiple scales. The proposed approach is fast, requires no input of user-defined parameter, produces well defined salient regions at full resolution and adapted to multi-scale image processing.