The classification and recognition of images play an important role in a number of applications

such as image retrieval

object detection

and video content analysis. Nowadays

a major breakthrough has been obtained based on deep convolution neural network (CNN) model

which has surpassed state-of-the-art methods for image classification and recognition

because the features extracted by CNN models are more discriminative and contain more semantic information than the traditional approaches. However

such CNN models as Alex-Net and ZFCNN-Net are extremely simple and incapable of extracting more information for representing images

while other models such as VGG16/VGG19 and GoogLeNet always have a huge number of neurons and parameters. In this work

a novel model named deep parallel cross CNN (PCCNN) is proposed

which can extract more effective information from images and has less neurons and parameters than other models. Inspired by the mechanism of human vision

which has two visual pathways and optic chiasma

the proposed PCCNN is designed based on the Alex-Net

which extracts two groups of CNN features in parallel through a couple of deep CNN data transform flows. Moreover

after the first fully connected layers in each stream

the information of two streams are fused together. The fused information is forwarded to the next two fully connected layers

and then the output information is fused again for more power representation features. Finally

for image classification

the Softmax regression function is employed with a 1024D image feature vector from the fusion of the two feature groups. Note that Alex-Net is used as the base model because of its simple architecture and its need to use fewer neurons. In the PCCNN model

the first stream is the original Alex-Net

and in the second stream

6 instead of 4 is used as the stride in the first convolutional layer. The larger stride in convolutional layer has worse performance if only a single stream is used because of the greater number of missing information. However

when the two streams are combined

the proposed model has better performance than all the other models. In addition

because a larger stride is used in the second stream

the feature maps are smaller

and the number of neurons and parameters are not greatly increased. Some popular public datasets

namely Caltech101

Caltech256

and Scene15

are selected to evaluate the performance of our model. At the same time

some state-of-the-art models are implemented with the same settings for comparison. Experimental results demonstrate that the proposed PCCNN model achieves better performance in terms of image classification than these models

indicating that the features extracted with the PCCNN model are more discriminative and have stronger presentation ability. On the Caltech101 dataset

the accuracy reaches approximately 63% on top1 with PCCNN model

exceeding that of the VGG16 model by about 5% and that of the GoogLeNet model by about 10%. Moreover

in terms of the Caltech256 dataset

our model also has better performance than the other models with accuracy of 46.4% on top1

surpassing those of the VGG16 and GoogLeNet models by 5% and 2.6%

respectively. However

our model has worse performance on Scene15 dataset than GoogLeNet

but still has higher accuracy than when only a single Alex-Net is used. The proposed PCCNN model has better performance than several state-of-the-art CNN models in terms of image classification and recognition

particularly on the medium-scale datasets

but on the small-scale dataset

the proposed model does not exhibit better performance. Hence

the model should be further tested on large-scale vision tasks

such as Imagenet or SUN dataset

which is the next work that the authors are planning to do. In fact

the PCCNN model is not only applicable to image classification and recognition

but it can also provide a novel thinking methodology for deep CNN model designing. In the deep CNN model

the deeper the architecture is

the more neurons and parameters exist

and the complexity also significantly increases. Thus

increase the width of the model can be increased to match the features and obtain better performance. Although this method also leads to an increase in the number of neurons and parameters

the rate of increase is slower than when more layers are added in the single model; furthermore

the model is more in line with the human visual physiological mechanism. Finally

the PCCNN model had great extendibility.