H Al-Sahaf, M Zhang, A Al-Sahaf… - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
The goodness of the features extracted from the instances and the number of training instances are two key components in machine learning, and building an effective model is …
R Churchill, L Singh, R Ryan… - Proceedings of the ACM …, 2022 - dl.acm.org
Researchers using social media data want to understand the discussions occurring in and about their respective fields. These domain experts often turn to topic models to help them …
P Lu, X Peng, C Yuan, R Li, X Wang - Neurocomputing, 2016 - Elsevier
The traditional color harmony models for the photo esthetics assessment, such as Moon & Spencer׳ s model and the adaptive hue template based approach, only utilize the …
Graph plays a very important role in graph based semi-supervised learning (SSL) methods. However, most current graph construction methods emphasize on local properties of the …
S Wang, EK Wang, X Li, Y Ye, RYK Lau, X Du - Knowledge-Based Systems, 2017 - Elsevier
Topic models, such as probabilistic latent semantic analysis (PLSA) and latent Dirichlet allocation (LDA), have shown impressive success in many fields. Recently, multi-view …
Image classification is a core task in many applications of computer vision, including object detection and recognition. It aims at analysing the visual content and automatically …
Latent topic models are applied to analyze the low-dimensional semantic meaning of documents and images, which are widely used in object categorization. However, the …
Supervised topic models leverage label information to learn discriminative latent topic representations. As collecting a fully labeled dataset is often time-consuming, semi …
Most of the existing methods for measuring the inter-concept distance (ICD) between two concepts from their image instances use only a single kind of visual feature extracted from …