Recognition improvement through optimized spatial support methodology

Abstract

Spatial support is an effective method of improving object recognition that is widely used in the field of computer vision. Compared to various other spatial support methods, such as sliding windows, spatial support based on image segmentation is a classic technique with high-quality segmentation that can positively contribute to image recognition. However, over-segmentation and under-segmentation often occur in the segmentation process. It is difficult for object classifiers to recognize objects in low-quality, segmented images, thus low-quality segmentation will reduce recognition accuracy. In order to resolve this drawback, watershed segmentation, multi-scale decompositions based on WLS (Weighted-Least Squares) filters and multi-layer smoothing, have been used to process the images. This methodology was utilized to maintain the sharp regions' boundaries, while smoothing regions which could contribute to accurately segmenting the images. After obtaining high-quality segmentation, the images could then be utilized in image recognition. The superiority of this paper's methodology compared to that of previous methods has been demonstrated herein. Experiments using a large database have demonstrated that this methodology is capable of improving image recognition through optimizing segmentation results.