Deep understanding of big multimedia data

Xiaofeng Zhu, Chong-Yaw Wee, Minjeong Kim
2020 Neural computing & applications (Print)  
The massive increase in the amount of data collected in business, social media, engineering sciences, and computer science makes accessing and analyzing structured, unstructured, and semi-structured data increasingly important [1, 2] . Ability to semantically understand the content of the data can substantially enhance the applications of big data. However, the performance of data understanding is heavily dependent on the learning techniques. In addition to volume, data are naturally comprised
more » ... f multiple representations or sources in real applications in which multi-source data provide enriched information from different perspectives [3, 4] . Multi-source data understanding is hence one of the most interesting and hottest topics in research and business nowadays and is remarkably useful for practical applications. Analyzing multi-source big data is a challenging task due to massive volume and multi-source structure of the data [5]. This paper attracts recent studies of multi-source data understanding as well the studies related to this domain to explore efficient and effective solutions of data analytics. In [6], Yang et al. propose a novel 'ASTR' model for end-to-end abstractive review summarization, which leverages the benefits of the supervised deep neural networks, reinforcement learning as well as the unsupervised probabilistic generative model for cross-domain abstractive review summarization. Experiment shows that the proposed model can generate better sentiment-aware summarization for reviews with different categories and aspects. In [7] , Tan et al. propose a new spectral clustering method based on mutual k-nn. The method uses mutual knn to learn the affinity matrix and then employ the affinity matrix to retain local information of the data. Furthermore, the method also utilizes the normalization method to further improve the performance of clustering. Experimental results on eight public data sets verified the effectiveness of the proposed method. In [8], Du et al. propose that a novel local covariance-based method to solve the problem of the traditional spectral clustering method often ignores the intersection between the different clusters of data. Specifically, the proposed method first learns an initial affinity matrix by adding the local covariance into traditional matrix construction step and then normalizes the obtained affinity matrix to further improve the clustering performance. In [9], Li et al. propose an unsupervised nonlinear feature selection method via kernel function. Specifically, the method first uses a kernel function to map data into a highdimensional space, making the data more distinguishable, and then utilize low-rank constraints to remove the influence of noise. Finally, the method employs L_21 norm conduct feature selection on kernel space. Experiment shows that the method achieves reasonable results on 12 public data sets. In [10], Zhou et al. propose a low-light enhancement model to solve the problem of imperfect lightness conditions usually lower the visual quality of an image, Specifically, with an input image, the model first generates multiple enhanced images based on a lightness-aware camera response model. These images are then fused at mid-level based on a patch-based image decomposition model. Experimental results show that the proposed model better improves the image quality in terms of visual naturalness and aesthetics. In [11] , Lu et al. propose a novel multitask learning approach called a hybrid representation-learning network
doi:10.1007/s00521-020-04885-9 fatcat:wiqlyp5kebdotkti7ljt3cr66m