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Material recognition is a fundamental problem in the field of computer vision. Material recognition is still challenging because of varying camera perspectives, light conditions, and illuminations. Feature learning or feature engineering helps build an important foundation for effective material recognition. Most traditional and deep learning-based features usually point to the same or similar material semantics from diverse visual perspectives, indicating the implicit complementary information (or crossmodal semantics) among these heterogeneous features. However, only a few studies focus on mining the cross-modal semantics among heterogeneous image features, which can be used to boost the final recognition performance. To address this issue, we first improve the well-known multiset discriminant correlation analysis model to fully mine the cross-modal semantics among heterogeneous image features. Then, we propose a novel hierarchical multi-feature fusion (HMF 2) model to gather effective information and create novel yet more effective and robust features. Finally, a general classifier is employed to train a new material recognition model. Experimental results demonstrate the simplicity, effectiveness, robustness, and efficiency of the HMF 2 model on two benchmark datasets. Furthermore, based on the HMF 2 model, we design an end-to-end online system for real-time material recognition.

Image sentiment analysis is a hot research topic in the field of computer vision. However, two key issues need to be addressed. First, high-quality training samples are scarce. There are numerous ambiguous images in the original datasets owing to diverse subjective cognitions from different annotators. Second, the cross-modal sentimental semantics among heterogeneous image features has not been fully explored. To alleviate these problems, we propose a novel model called multidimensional extra evidence mining (ME 2 M) for image sentiment analysis, it involves sample-refinement and cross-modal sentimental semantics mining. A new soft voting-based sample-refinement strategy is designed to address the former problem, whereas the state-of-the-art discriminant correlation analysis (DCA) model is used to completely mine the cross-modal sentimental semantics among diverse image features. Image sentiment analysis is conducted based on the cross-modal sentimental semantics and a general classifier. The experimental results verify that the ME 2 M model is effective and robust and that it outperforms the most competitive baselines on two well-known datasets. Furthermore, it is versatile owing to its flexible structure.

Recommendation system for tourist spots has very high potential value including social and economic benefits. The traditional clustering algorithms were usually used to build a recommendation system. However, clustering algorithms have the risk on falling into local minimums, which may decrease the final recommendation performance heavily. Few works focused their research on tourist spots recommendation and few recommendation systems consider the population attributes information for fitting the user implicit preference. To address the problem, we focused our research work on designing a novel recommendation system for tourist spots. First a new dataset named “Smart Travel” is created for the following experiments. Then hierarchical sampling statistics (HSS) model is used to acquire the user preference for different population attributes. A new recommendation list named LA is generated in turn by fitting the excavated the user preference. More importantly, SVD++ algorithm rather than those traditional clustering algorithms is used to predict the user ratings. And a new recommendation list named LB is generated in turn on the basis of rating predictions. Finally, the two lists LA and LB are fused together to boost the final recommendation performance. Experimental results demonstrate that the mean precision, mean recall, and mean F1 values of the proposed recommendation system improve about 7.5%, 6.2%, and 6.5%, respectively, compared to the best competitor. The novel recommendation system is especially better at recommending a group of tourist spots, which means it has higher practical value.

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