Convolutional Neural Networks Based Image Classification

As one of the traditional topics of computer vision research area,image classification consists of fields like hand written recognition,natural image classification,and hyperspec-tral image(HSI)classification,etc.With the development of advanced computing theory and algorithms,various solutions have boosted the performance of image classification problems.Recently,under the promotion of large scale images and extremely fast computing resources,deep learning becomes the most efficient method for classification problems.However,no standardized solution is available for tremendous image classification applications.Therefore,the design of neural network architecture with consideration of image characters is a promising topic.This thesis concentrates on the influence of data analysis to model structure design,when we use deep neural network models as image classification solutions.Especially,our work focuses on the improvement on model performance with limited trainable parameters,and the generalization of deep models with limited data.The main contribution of this thesis are listed as below:1.We propose the introduction of contextual information to convolutional neural network(CNN)models on image pixel labeling tasks.Contextual information constraints highly affect the image pixel labeling(i.e.image semantic segmentation)task.For CNN based methods,the concept of receptive field plays an essential role.In order to obtain a larger receptive field,we could either alter to bigger convolutional kernels or introduce deeper layers.Neither of those two tricks,however,can neglect the booming of trainable parameters.In our solution,we propose the nonlocal convolutional kernel,which can gain a larger receptive field with the increase in steps.As to contextual information,we have designed the context aware module and built up a context aware nonlocal neural network.Compared with conventional CNN models,the proposed method can get a larger receptive field and thereby better discriminative features.With the same number of parameters,the proposed method can get superior image classification performance on public datasets.2.We propose a CNN based HSI classification method with consideration of band sensi-tivity.Previous CNN based HSI classification methods directly apply convolutional operations on the spectral channel,which ignore the large range difference between inconsistent spectrum.This could increase the need for parameters and also brings unnecessary co-adaption during training.What's more,the labeled data remain limited nowadays.The two concerns mentioned previously could affect the model's generalization ability.In this paper,we propose analysing the difference between spectrum and design a CNN model accordingly.We cluster the spec-trum into different groups.Within each group,the spectrum is highly correlated.In the next,we extract local features from those groups and concatenate them for the final decision.Com-paring with other CNN models,the proposed model can get a higher performance with fewer parameters with the advantage of band sensitive prior information.3.We propose the CNN based HSI classification method with spatial pixel pair features.Although the contextual information is not that strong as natural images,neighboring coherent still weights for HSI images.Conventional CNN models directly use the spatial information as one auxiliary convolutional kernel,which highly overlooks the structure of the local neighbor-hood.In this paper,we propose using locally structured pixel pairs for better feature representa-tion.Meanwhile,we also build up a multi-channel CNN framework to fit in with the proposed features,which can be adaptive to the embedding of various sub-networks.Experimental results further confirm the proposed method's efficiency.4.We propose using raw spherical as input for CNN to generate guidance signals for mobile robots.The capture of images of assorted poses is a great challenge to robot navigation task.Conventionally,we could use a group of monocular cameras to capture images of fixed angles.In reality,however,this solution cannot be adaptive to the demanding of various precise angles.In this paper,we propose using the spherical camera to capture the 360~?scene.With the help of spherical cameras,we could easily capture videos and generate target images with any angle desired.For better proof of the proposed method,we have built up a Spherical-Navi dataset,which consists of different scenes of changing illumination conditions.What's more,we build one CNN model with batch normalization layers,which could help us to get a better classification with highly similar spherical images.Simulation experiments and outdoor navigation experiments both demonstrate the confidence of this method in reality.