An active contour segmentation using the perspective box concept for figure-ground object detection

Abstract

Image segmentation has been widely employed as a vital pre-processing phase in content-based image retrieval system, object tracking system, camera surveillance system, etc. Image segmentation procedure helps visual system to detect and to recognise main objects in a digital image scene. The active contour approach is a common technique used in the initial processes to detect objects. According to previous researches, the active contour approach used in region-based segmentation procedures had shown impressive results in segmenting an image scene into different object categories. However, when dealing with a complex image scene, the active contour approach was unable to segment the figure-ground and background of the given objects. Therefore, this thesis proposes an alternative method in image segmentation based on the perspective box concept, to detect main objects. The proposed concept introduces vanishing points to model an image scene based on the visual attention concept. The vanishing points are used to identify the relevant image information or attention points in the scene by using a Hough transform. Once the points of attention in the objects have been identified, the region of interest will be minimised in the active contour segmentation process. Then, the statistical region merging algorithm is used to construct multiple layers of the image object in order to produce the bounding box coordinate in XML codes. For benchmarking purposes, the codes were compared with PASCAL Visual Object Classes Challenge 2010 (VOC 2010) dataset by using three performance parameters namely precision, recall and F-measure. The results have shown that the average detection rate is more than 50%. Therefore, the proposed approach outperforms the active contour segmentation technique. In addition, procedures of the perspective box concept can be carried out automatically without any manual intervention or reliance on intelligent systems.