Research On Image Expression And Classification Based On Sparse And Low-rank Models

Image expression and classification are two of the most important issues in computer vision,pattern recognition and image processing.They are also the fundamental modulars of many computer vision and image processing tasks,such as image understanding,object detection and tracking,and image retrieval,which can directly affect the performances of successive works.The traditional machine learning methods are confronted with difficulties in dealing with high dimensional and complex data.Sparse and low rank models have recently emerged as advanced techniques in the field of computer vision and machine learning.Based on subspace learning,graph based semi-supervised learning and extreme learning machine,we apply sparse and low rank models for the problems of image expression and classification.The main contributions and results of this thesis are summarized as follows:?1?Image feature extraction based on robust weighted group sparse representationTraditional sparse representation model suffers from the problems of unsupervision,random selection and weak robustness.These problems might results in less discriminative feature obtained by sparse representation based feature extraction methods,based on which unsatisfied classification performance might be achieved.On these problems,a supervised robust weighted group sparse representation model is proposed by minimizing the combination of l₁ norm regularized representation fidelity and weighted l_2,1 norm regularized representation coefficients.The model can harness the label information to get stable and robust group sparse representation,and adaptively determine the intra-class and inter-class neighbors and the similarity relationships for each sample.Low dimensional subspace is learnt using the intra-class and inter-class graphs derived from the group sparse representation to get discriminative low-dimensional subspace,based on which low-dimensional and discriminative feature of high dimensional image data can be calculated.Experimental results show that the proposed method is more robust to noise and occlusions,and the feature extracted by our method can achieve promising classification accuracy in comparison with related methods.?2?Image feature extraction based on block-diagonal constrained low rank and sparse representationSpare representation can only reveal the local geometry structure of data,and cannot discover the global structure relationship among data.The drawback might potentially degrade the discriminability of obtained feature and classification accuracy.For the problem,a block-diagonal constrained low rank and sparse representation model is developed by taking the merits of both sparse and low rank representation models.The developed model can effectively discover both local geometry and global multi-subspace structures in data.Besides,the introduction of the block-diagonal constraint can utilize the label prior.Under the principal of sample representation,the representation coefficient obtained by the model can highlight interclass differences and enhance inter-class similarities to get block-diagonal low rank and sparse representation.The representation is further utilized to learn low dimensional subspace,where data points from the same class gather and data points from different classes separate.Compared with other methods,the feature extracted by our method is more discriminative with higher classification accuracy.?3?Label and locality constrained low rank graph learningOn the problem that label and locality information is not well exploited in existing graph learning methods,a graph learning method is presented by fusing low rank constraint,label information and locality information of data.The model can effectively harness the precious label information,and the introduction of locality information will make the graph be sparse.As a result,the optimized graph has the traits of low rank,label guiding,and locality preserving?sparsity?.The proposed model is further applied for graph based semi-supervised classification problem.Experimental results show that the obtained graph can well reveal the affinity relationships of samples,and the label information can effectively propagate from the labeled data to the unlabeled ones to fulfill the task of semi-supervised classification.?4?Neuron-pruning based discriminative Extreme Learning Machine and its hierarchical learningThis section studies three problems of Extreme Learning Machine?ELM?for the problems of image feature learning and classification.?1?The improvement of generalization ability of ELM network.A discriminative Extreme Learning Machine model?DELM?is developed.DELM introduces a nonnegative label slack matrix to relax the strict and fixed target label matrix of traditional ELM to be a learnable and flexible label matrix.The strategy can provide more freedom for the learning of output weights matrix to enlarge the distance between transformed inter-class samples.Experimental results show that this strategy can better discover the discriminative information in data,and improve the generalization ability of the obtained network with higher classification accuracy.?2?Structure design of ELM network.Based on DELM,a neuron pruning based DELM model is developed by restricting the output weights matrix to be row spare with l_2,1 norm regularization.The strategy can distinguish the importance of different neurons in information processing and prune the worthless ones.As a result,the neurons needed in hidden layer can be adaptively determined for a more compact network.Experimental results show that the proposed model can reduce both the storage needs for neurons and the prediction time with comparative or even better classification performance.?3?Feature learning and classification based on multilayer ELM.A multilayer extreme learning machine is presented by adopting row sparse constraint and label relaxation strategy.An extreme learning machine based auto-encoder is developed with row sparsity constraint to adaptively determine the neurons needed in hidden layer.With this manner,the auto-encoder is learnt and stacked layer by layer to get deep neural network for unsupervised feature learning.For the supervised classification of obtained feature,the label relaxation strategy is employed to calculate the output weight matrix.Experimental results show that the obtained network is light and compact with promising classification performance.