Due to the digitization of fine art collections, pictures of fine art objects stored at museums and art galleries became widely available to the public. It created a demand for efficient software tools that would allow rapid retrieval and semantic categorization of art. This paper introduces a new, two-stage image classification approach aiming to improve the style classification accuracy. At the first stage, the proposed approach divides the input image into five patches and applies a deep convolutional neural network (CNN) to train and classify each patch individually. At the second stage, the outcomes from the individual five patches are fused in the decision-making module, which applies a shallow neural network trained on the probability vectors given by the first-stage classifier. While the first stage categorizes the input image based on the individual patches, the second stage infers the final decision label categorizing the artistic style of the analyzed input image. The key factor in improving the accuracy compared to the baseline techniques is the fact that the second stage is trained independently on the first stage using probability vectors instead of images. This way, the second stage is effectively trained to compensate for the potential mistakes made during the first stage. The proposed method was tested using six different pre-trained CNNs (AlexNet, VGG-16, VGG-19, GoogLeNet, ResNet-50, and Inceptionv3) as the first-stage classifiers, and a shallow neural network as a second-stage classifier. The experiments conducted using three standard art classification datasets indicated that the proposed method presents a significant improvement over the existing baseline techniques. INDEX TERMS Fine art style recognition, painting classification, machine learning, multi-stage classification, transfer learning, digital humanities.

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An automatic classification of fine art images is limited by the scarcity of high-quality labels made by art experts. This study aims to provide meaningful automatic labeling of fine art paintings (machine labeling) without the need for human annotation. A new unsupervised Adversarial Clustering System (ACS) is proposed. The ACS is an adversarial learning approach comprising an unsupervised clustering module generating machine labels and a supervised classification module classifying the data based on the machine labels. Both modules are linked through an optimization algorithm iteratively improving the unsupervised clusters. The objective function driving the improvement consists of the within-cluster sum of squares (WCSS) error and the supervised classification accuracy. The proposed method was tested on three different fine-art datasets, including two sets of paintings previously categorized by art experts and one never categorized collection of Australian Aboriginal paintings. The unsupervised clusters were analyzed using standard unsupervised clustering metrics and a reliability measure between machine and human labeling. The ACS showed higher reliability compared to the classical k-means clustering method. The content analysis of unsupervised clusters indicated grouping based on scene composition, type, and shape of the object, edge sharpness and direction, and color palette.

Automatic prediction of team performance and workload plays a crucial role in team selection, training, evaluation, and re-training processes. This study investigated the potential of using voice analysis of team-based communication for predicting team workload (TW) and team performance (TP). Both the TW and TP categories were labeled objectively. Ten teams of three participants were tasked with completing a computer-based command-and-control simulation that required communication of task-specific information to each team member. Recordings of each participant's voice communications were used to train Convolution Neural Network (CNN) models for each team separately. It was hypothesized that integrating TW and TP information into the prediction process would support the prediction of both TW and TP categories. Two experiments were conducted. In the first experiment, the TP prediction networks were fine-tuned to predict TW, and conversely, the TW prediction networks were fine-tuned to predict TP. In the second experiment, the TP or TW prediction based on the assembly of interconnected TP and TW classifiers was tested. Both experiments confirmed the hypothesis. It was shown that task-related pre-requisite knowledge embedded into the neural network reduced neural network model training time and improved performance without the need to increase the training data size. Predictions based on combined TW and TP classification outcomes -using either separate or interconnected TW or TP classifiers -outperformed the baseline method using a single CNN model trained to predict either TW or TP alone. The classification accuracy was consistent with previously reported cognitive load prediction based on objective measures.

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