Anuruk Prommakhot, Jakkree Srinonchat^*

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3313-3329, 2022, DOI:10.32604/cmc.2022.024417

Abstract Image classification always has open challenges for computer vision research. Nowadays, deep learning has promoted the development of this field, especially in Convolutional Neural Networks (CNNs). This article proposes the development of efficiently scaled dilation of DropBlock optimization in CNNs for the fungus classification, which there are five species in this experiment. The proposed technique adjusts the convolution size at 35, 45, and 60 with the max-polling size 2 × 2. The CNNs models are also designed in 12 models with the different BlockSizes and KeepProp. The proposed techniques provide maximum accuracy of 98.30% for the training set. Moreover, three accurate models,… More >

Wei Sun^1,2,*, Xuan Chen³, Xiaorui Zhang^1,3, Guangzhao Dai², Pengshuai Chang², Xiaozheng He⁴

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3549-3561, 2021, DOI:10.32604/cmc.2021.021627

Abstract Vehicle re-identification (ReID) aims to retrieve the target vehicle in an extensive image gallery through its appearances from various views in the cross-camera scenario. It has gradually become a core technology of intelligent transportation system. Most existing vehicle re-identification models adopt the joint learning of global and local features. However, they directly use the extracted global features, resulting in insufficient feature expression. Moreover, local features are primarily obtained through advanced annotation and complex attention mechanisms, which require additional costs. To solve this issue, a multi-feature learning model with enhanced local attention for vehicle re-identification (MFELA) is proposed in this paper.… More >