Multi-scale graph theoretic image segmentation using wavelet decomposition

Abstract

We present a novel implementation of multi-scale graph-theoretic image segmentation using wavelet decomposition. This bottom-up segmentation through a weighted agglomeration approach utilizes the specific statistical characteristics of vehicles to quickly detect regions of interest in image frames. The method incorporates pixel intensity, texture, and boundary values to detect salient segments at multiple scales. Wavelet decomposition creates gradient and image approximations at multiple scales for fast edge weighting between nodes in the graph. Nodes with strong edge weights merge to form a single node at a higher level, where new internal statistics are calculated and edges are created with nodes at the new scale. Top-down saliency energy values are then calculated for each pixel on every scale, with the pixel labeled as a member of the node (segment) at the scale of highest energy. Salient node information is then used for binary classification as a potential object or non-object passes to classification and tracking algorithms. The method provides multi-scale segmentations by agglomerating nodes that consist of finer node agglomerations (lower scales). Criteria for weights between nodes include multi-level features, such as average intensity, variance, and boundary completion values. This method has been successfully tested on an electro-optical (EO) data set with multiple varying operating conditions (OCs). It has been shown to successfully segment both fully and partially occluded objects with minimal false alarms and false negatives. This method can easily be extended to produce more accurate segmentations through the sensor fusion of registered data types.