Zhang

AAAI Conferences

We propose a novel approach to semantic segmentation using weakly supervised labels. In traditional fully supervised methods, superpixel labels are available for training; however, it is not easy to obtain enough labeled superpixels to learn a satisfying model for semantic segmentation. By contrast, only image-level labels are necessary in weakly supervised methods, which makes them more practical in real applications. In this paper we develop a new way of evaluating classification models for semantic segmentation given weekly supervised labels. For a certain category, provided the classification model parameter, we firstly learn the basis superpixels by sparse reconstruction, and then evaluate the parameters by measuring the reconstruction errors among negative and positive superpixels. Based on Gaussian Mixture Models, we use Iterative Merging Update (IMU) algorithm to obtain the best parameters for the classification models. Experimental results on two real-world datasets show that the proposed approach outperforms the existing weakly supervised methods, and it also competes with state-of-the-art fully supervised methods.


Enhancing the Performance of Semi-Supervised Classification Algorithms with Bridging

AAAI Conferences

Traditional supervised classification algorithms require a large number of labelled examples to perform accurately. Semi-supervised classification algorithms attempt to overcome this major limitation by also using unlabelled examples. Unlabelled examples have also been used to improve nearest neighbour text classification in a method called bridging. In this paper, we propose the use of bridging in a semi-supervised setting. We introduce a new bridging algorithm that can be used as a base classifier in any supervised approach such as co-training or selflearning. We empirically show that classification performance increases by improving the semi-supervised algorithm's ability to correctly assign labels to previouslyunlabelled data.


Latent Multi-view Semi-Supervised Classification

arXiv.org Artificial Intelligence

To explore underlying complementary information from multiple views, in this paper, we propose a novel Latent Multi-view Semi-Supervised Classification (LMSSC) method. Unlike most existing multi-view semi-supervised classification methods that learn the graph using original features, our method seeks an underlying latent representation and performs graph learning and label propagation based on the learned latent representation. With the complementarity of multiple views, the latent representation could depict the data more comprehensively than every single view individually, accordingly making the graph more accurate and robust as well. Finally, LMSSC integrates latent representation learning, graph construction, and label propagation into a unified framework, which makes each subtask optimized. Experimental results on real-world benchmark datasets validate the effectiveness of our proposed method.


The Hitchhiker's Guide to Machine Learning in Python

#artificialintelligence

Machine learning is undoubtedly on the rise, slowly climbing into'buzzword' territory. This is in large part due to misuse and simple misunderstanding of the topics that come with the term. Take a quick glance at the chart below and you'll see this illustrated quite clearly thanks to Google Trends' analysis of interest in the term over the last few years. However, the goal of this article is not to simply reflect on the popularity of machine learning. It is rather to explain and implement relevant machine learning algorithms in a clear and concise way.


The Hitchhiker's Guide to Machine Learning in Python

#artificialintelligence

Machine learning is undoubtedly on the rise, slowly climbing into'buzzword' territory. This is in large part due to misuse and simple misunderstanding of the topics that come with the term. Take a quick glance at the chart below and you'll see this illustrated quite clearly thanks to Google Trends' analysis of interest in the term over the last few years. However, the goal of this article is not to simply reflect on the popularity of machine learning. It is rather to explain and implement relevant machine learning algorithms in a clear and concise way.