Shape Based Object Detection and Recognition in Silhouettes and Real Images

Abstract

Shape is very essential for detecting and recognizing objects. It is robust to illumination, color changes. Human can recognize objects just based on shapes, thus shape based object detection and recognition methods have been popular in many years. Due to problem of segmentation, some researchers have worked on silhouettes instead of real images. The main problem in this area is object recognition and the difficulty is to handle shapes articulation and distortion. Previous methods mainly focus on one to one shape similarity measurement, which ignores context information between shapes. Instead, we utilize graph-transduction methods to reveal the intrinsic relation between shapes on 'shape manifold'. Our methods consider the context information in the dataset, which improves the performance a lot. To better describe the manifold structure, we also propose a novel method to add synthetic data points for densifying data manifold. The experimental results have shown the advantage of the algorithm. Moreover, a novel diffusion process on Tensor Product Graph is carried out for learning better affinities between data. This is also used for shape retrieval, which reaches the best ever results on MPEG-7 dataset. As shapes are important and helpful for object detection and recognition in real images, a lot of methods have used shapes to detect and recognize objects. There are two important parts for shape based methods, model construction and object detection, recognition. Most of the current methods are based on hand selected models, which is helpful but not extendable. To solve this problem, we propose to construct model by shape matching between some silhouettes and one hand decomposed silhouette. This weakly supervised method can be used not only learn the models in one object class, but also transfer the structure knowledge to other classes, which has the similar structure with the hand decomposed silhouette. The other problem is detecting and recognizing objects. A lot of methods search the images by sliding window to detect objects, which can find the global solution but with high complexity. Instead, we use sampling methods to reduce the complexity. The method we utilized is particle filter, which is popular in robot mapping and localization. We modified the standard particle filter to make it suitable for static observations and it is very helpful for object detection. Moreover, The usage of particle filter is extended for solving the jigsaw puzzle problem, where puzzle pieces are square image patches. The proposed method is able to reach much better results than the method with Loopy Belief Propagation.