Abstract
This thesis investigates three research topics: object extraction, face recognition, and vehicle verification. First, we develop an object extraction method based on the model-based background subtraction. Different from previous methods, we introduce a hybrid codebook-based background subtraction method by combining the mixture of Gaussian (MOG) with the codebook (CB) method. The so-called ellipsoid CB model for modeling the dynamic background with highlight and shadow is a modified shadow/highlight removal method which can overcome the influence of illumination change. It can avoid extracting the false foreground pixels (e.g., dark background) or missing the real foreground pixels (e.g., bright foreground). Finally, we show two experiments to compare our method with the others based on the change detection benchmark dataset provided in CVPR 2011. Second, we propose an appearance-based face recognition method. Most of the appearance-based methods use multiple samples per person for training. However, normally we do not have enough training samples for each person. The appearance-based methods may not work due to insufficient training samples. Therefore, we modify the Discriminative Multi-manifold Analysis (DMMA) method and propose an acceleration method. Our fast DMMA method can be divided into three modules. First, we input the training samples of multiple persons, one person one training sample, and then use a modified of K-means method to identify the similarity of two groups people. Second, these two groups of faces are divided into non-overlapping local patches for the DMMA. Third, we repeat the previous two steps to obtain the binary tree projection matrix of fast DMMA. The accelerated DMMA shows very little accuracy deficiency. Third, verifying the same vehicle appearing in two scenes is a nontrivial problem that cannot be solved by corresponding feature matching. Here, we propose a new sparse representation (SR) for vehicle verification using the Boost K-SVD method, which offers more effective object representation. First, we use particle filtering to find the initial atom. Next, we generate the dictionary satisfying the nearly orthonormal property as similar as Restricted Isometry Property. Finally, we use a discrimination criterion to determine the number of atoms for enhanced verification accuracy. The vehicles in two views are subsequently combined and represented as a feature pair, each of which can be either a positive or negative pair. The verification is simplified as a binary classification problem. The contributions of the proposed Boost K-SVD method are (1) generating a proper SR dictionary, (2) finding the initial atom more quickly, and (3) improving verification accuracy.