Texture-Based Infrared Image Segmentation by Combined Merging and Partitioning

This paper describes a method of image segmentation using recursive splitting and merging based on texture similarity measures. This technique addresses the problem of segmenting image regions of varying texture with limited intensity edges. The proposed technique provides a framework for texture based image segmentation that is shown to be applicable across a wide variety of image content. The primary motivation for this work is the segmentation of infrared images. Infrared imagery is characterized by narrow histograms corresponding to ambient scene temperature. Results illustrate that using texture signatures for infrared imagery yields enhanced segmentation performance over luminance features. Additional benefit for infrared imagery and better generality to other images types is obtained when luminance and texture are both applied to the segmentation criteria. A method for the quantitative comparison of segmentation results is presented and benchmarks are provided against several recent segmentation algorithms.