Classification-Based Probabilistic Modeling of Texture Transition for Fast Line Search Tracking and Delineation

Abstract

Abstract-We introduce a classification-based approach to finding occluding texture boundaries. The classifier is composed of a set of weak learners which operate on image intensity discriminative features which are defined on small patches and fast to compute. A database which is designed to simulate digitized occluding contours of textured objects in natural images is used to train the weak learners. The trained classifier score is then used to obtain a probabilistic model for the presence of texture transitions which can readily be used for line search texture boundary detection in the direction normal to an initial boundary estimate. This method is fast and therefore suitable for real-time and interactive applications. It works as a robust estimator which requires a ribbon like search region and can handle complex texture structures without requiring a large number of observations. We demonstrate results both in the context of interactive 2-D delineation and fast 3-D tracking and compare its performance with other existing methods for line search boundary detection.

Human head-shoulder segmentation

“…Dollar, Tu and Belongie [5] use a large number of generic features across different scales to train a boosting tree for detection. Shahrokni et al [4] use some simple feature on ribbon patch.…”

“…To refine the segmentation border further, we explore the border detection method proposed in [4]. We extract image patches on the normal direction of the borders from ground truth.…”

“…Learning based border and edge detection based on low level features has shown to be highly adaptive and effective [4,5,6,7]. Shahrokni et al [4] trained a detector by AdaBoost for texture boundary detection.…”

“…Shahrokni et al [4] trained a detector by AdaBoost for texture boundary detection. Their results showed accurate object borders identified as texture transitions in complex scenes.…”

“…Start with an extended face area as the region of interest, to improve the speed of graph cut, watershed segmentation is used first to convert this region into a superpixel image; graph cut with head-shoulder constraint is then applied to the superpixel level graph and followed by a special hair region processing to get a border that segments the head-shoulder region from its background. For boundary refinement, we explore the method of border detection through machine learning as [4]. A border detector is trained using AdaBoost where ribbon patches along the segmented border in the normal direction is used as positive samples and patches away from border as negative samples.…”