Image Object Classfication Research Based On Discriminative Learning

The analysis and understanding of the image content is one of the important contents for the visual intelligence,the image object classification is a research focus in the field of the analysis and understanding for the image content,there are very important applications by the image object classification in the practical life which has been researched widely.Currently,the basic thinking of the imgae object classification is firstly building the image object presentation,secondly learning the image object class by the machine learning,and then classifying or recognizing the the unseen image objects by the learned models.The semantic gap occurs between the low level features represented by computers and the high level semantic features understood by the human,it makes the image object classification face the great challenges,and the image object classificaiton should be researched further.Because the discrimative learning has the good practical ability,this thesis mainly researchs how to fuse the imgae object presentation and the discriminative learning in order to classify the image objects.This thesis researchs the image object classification mainly from two great issues including the general image object classification and the special image object classification,for the general image object classification the fusion of the local feature based image presentation and the discriminative learning is used,and for the special image object classification,the different global features can be extracted according to the characteristic of the special image objects,and then the corresponding discriminative classification methods are combined to classify the image objects.The main research works and creative points in this thesis are summed up as followings:1.Sufficiently mining the structural characteristic of feature space from local features,the density-guided tree-structured kernel is proposed,which is a non-paramatic kernel,has the linear compuation cost with the number of feature points,can compute the partial matching relations between two feature sets with unequal cardinality,has better matching ability,does not require the users specify the special paramaters,satisfies the positive define condition,can be used to the kernel based learning algorithms,can fuse the image object presentation and classifier well, and can also locate or recognize the image objects.The experimental results show that the prosed kernel has the good matching ability and the image object classification ability.2.Researching the location correlation from the local features in the image space, local feature spatial correlation kemel is proposed,which can describe the relative location relationship from the local features in the image space,satisfies the positive define condition,can be emmbeded into kemel based learning algorithm,and has the better time efficiency.The experimental results show the local feature spatial correlation kernel has the better classification ability.3.Researching the relations for local features in both image space and feature space,the bi-space pyramid matching kernel is proposed,which can satisfy positive define condition,has linear computation cost,can be emmbeded into kernel based learning algorithm.The experimental results show the bi-space pyramid matching kernel has better classification performance.4.Carefully analysing the semantic content of the remote sensing images,a hierarchical model of semantemes for remote sensing images is designed,which can be used to the remote sensing image classification,retrieval,object detection and recognition,etc.Comer distribution based airplane detection in the middle/low resolution remote sensing images is also proposed,the coarse locations of the object can be achieved fastly using comer distribution feature of the airplane,the computation cost for the classification can be reduced,and then the airplane can be discriminated using the simple and efficient spatial structure feature and the decision tree.The experment achieves the good performance.5.Aiming at the automatic recognition of the camera-based chinese and english character language types,the posterior probability estimation based cascade classifier is proposed,in which the discriminative learning algorithm is used,there are two methods for designing the node thresholds of cascade classifier,such as independent threshold designing and dependent threshold designing,and the cascade classifier satisfying the whole requirements is designed from the theory.The designing of the proposed cascade classifier can provide a theoretic method to design the classifier with high classificaiton rate.In order to mine the structure difference of the chinese and english characters,the gradient information of the pixel based horizontal and vertical stroke vector,gradient orientation correlogram and the relative gray information of the location correlation pixels based census transform histogram are used,they can be robust to illumination,noise and resolution and so on,and can be applied to camera-based images.Both the theoretic analysis and experimental results show that the dependent threshold designing can make the cascade classifier achieve the higher classification rate,the proposed method has the good classification performance to the camera-based chinese and english character language types.