Improved contour and texture-based object segmentation

The objective of this work is the detection of object classes. An improved method is used for object detection and segmentation in real-world multiple-object scenes. It has two stages. In the first stage this method develops a novel technique to extract class-discriminative boundary fragments and the texture features near the boundary, and then boosting is used to select discriminative boundary fragments (weak detectors) to form a strong ¿boundary-fragment-model¿ detector. An appearance model is built with those entire detectors and the texture features. In the second stage, the boundary fragment and the texture features and used to complete detection. To the end, a new fast cluster algorithm is used to deal with the centroid image. The generative aspect of the model is used to determine an approximate segmentation. In addition, we present an extensive evaluation of our method on a standard dataset and compare its performance to existing methods from the literature. As is shown in the experiment, our method outperforms previously published methods with the overlap part of the object in multiple-object scene.