School of Art and Design, Wuhan University of Technology, Wuhan 430070, China.
Glasgow School of Art, Glasgow Scotland G4 9LE, UK.
Comput Intell Neurosci. 2022 Apr 12;2022:2836486. doi: 10.1155/2022/2836486. eCollection 2022.
Nowadays, the information processing capabilities and resource storage capabilities of computers have been greatly improved, which also provides support for the neural network technology. Convolutional neural networks have good characterization capabilities in computer vision tasks, such as image recognition technology. Aiming at the problem of high similarity image recognition and classification in a specific field, this paper proposes a high similarity image recognition and classification algorithm fused with convolutional neural networks. First, we extract the image texture features, train different types, and different resolution image sets and determine the optimal texture different parameter values. Second, we decompose the image into subimages according to the texture difference, extract the energy features of each subimage, and perform classification. Then, the input image feature vector is converted into a one-dimensional vector through the alternating 5-layer convolution and 3-layer pooling of convolutional neural networks. On this basis, different sizes of convolution kernels are used to extract different convolutions of the image features, and then use convolution to achieve the feature fusion of different dimensional convolutions. Finally, through the increase in the number of training and the increase in the amount of data, the network parameters are continuously optimized to improve the classification accuracy in the training set and in the test set. The actual accuracy of the weights is verified, and the convolutional neural network model with the highest classification accuracy is obtained. In the experiment, two image data sets of gems and apples are selected as the experimental data to classify and identify gems and determine the origin of apples. The experimental results show that the average identification accuracy of the algorithm is more than 90%.
如今，计算机的信息处理能力和资源存储能力得到了极大的提高，这也为神经网络技术提供了支持。卷积神经网络在计算机视觉任务中具有良好的特征描述能力，例如图像识别技术。针对特定领域中高相似度图像识别和分类的问题，本文提出了一种融合卷积神经网络的高相似度图像识别和分类算法。首先，我们提取图像纹理特征，训练不同类型和不同分辨率的图像集，并确定最佳纹理不同参数值。其次，我们根据纹理差异将图像分解为子图像，提取每个子图像的能量特征，并进行分类。然后，通过卷积神经网络的交替 5 层卷积和 3 层池化，将输入图像特征向量转换为一维向量。在此基础上，使用不同大小的卷积核提取图像特征的不同卷积，并使用卷积实现不同维度卷积的特征融合。最后，通过增加训练次数和增加数据量，不断优化网络参数，提高训练集和测试集的分类精度。验证权重的实际精度，获得分类精度最高的卷积神经网络模型。在实验中，选择宝石和苹果的两个图像数据集作为实验数据进行分类和识别宝石，并确定苹果的产地。实验结果表明，该算法的平均识别准确率超过 90%。
