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While artists demonstrate their individual styles through paintings and drawings, how to describe such artistic styles well selected visual features towards computerized analysis of the arts remains to be a challenging research problem. In this paper, we propose an integrated feature-based artistic descriptor with Monte Carlo Convex Hull (MCCH) feature selection model and support vector machine (SVM) for characterizing the traditional Chinese paintings and validate its effectiveness via automated classification of Chinese paintings authored by well-known Chinese artists. The integrated artistic style descriptor essentially contains a number of visual features including a novel feature of painting composition and object feature, each of which describes one element of the artistic style. In order to ensure an integrated discriminating power and certain level of adaptability to the variety of artistic styles among different artists, we introduce a novel feature selection method to process the correlations and the synergy across all elements inside the integrated feature and hence complete the proposed style-based descriptor design. Experiments on classification of Chinese paintings via a parallel MCCH model illustrate that the proposed descriptor outperforms the existing representative technique in terms of precision and recall rates.

Unsupervised crowd counting is a challenging yet not largely explored task. In this paper, we explore it in a transfer learning setting where we learn to detect and count persons in an unlabeled target set by transferring bi-knowledge learnt from regression-and detection-based models in a labeled source set. The dual source knowledge of the two models is heterogeneous and complementary as they capture different modalities of the crowd distribution. We formulate the mutual transformations between the outputs of regression-and detection-based models as two scene-agnostic transformers which enable knowledge distillation between the two models. Given the regression-and detection-based models and their mutual transformers learnt in the source, we introduce an iterative self-supervised learning scheme with regression-detection bi-knowledge transfer in the target. Extensive experiments on standard crowd counting benchmarks, ShanghaiTech, UCF_CC_50, and UCF_QNRF demonstrate a substantial improvement of our method over other state-of-the-arts in the transfer learning setting. CCS CONCEPTS • Information systems → Multimedia information systems; • Humancentered computing → Collaborative and social computing; Visualization; • Computing methodologies → Computer vision.

Semantic relation extraction is a crucial step of automatically constructing a knowledge graph from unstructured biomedical text. Many real-world applications can benefit from it. As unsupervised relation extraction approaches, generative probabilistic models, Rel-LDA and Type-LDA, are receiving more attention in recent years. However, these two models inherit the bag-of-word assumption of the standard LDA model, which disable the exploitation of more distinguishable n-gram features. To overcome this limitation, two alternative models, named as Rel-TNG and Type-TNG, are proposed with the help of Topic N-Grams (TNG) model in this study, and collapsed Gibbs sampling algorithm is utilized for inference. Extensive experimental results on GENIA and EPI corpora indicate that Rel-TNG and Type-TNG models have similar performance with their unigram counterparts, but Rel-TNG and Type-TNG models outperform Rel-LDA and Type-LDA models when prior knowledge is available.