Label Distribution Learning

Geng, Xin

doi:10.1109/tkde.2016.2545658

Cited by 539 publications

(410 citation statements)

References 31 publications

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“…Several work incorporate the model weights learned from a large-scale general dataset [Deng et al, 2009] and finetune the state-of-the-art CNNs for the task of visual emotion prediction [Campos et al, 2015]. [You et al, 2015;2016] propose a novel progressive CNN architecture, namely PCNN, to make use of large noisy web data, and further perform benchmarking analysis on the Flickr and Instagram (FI) dataset, which is currently the largest single label dataset containing 23,308 affective images. In [Rao et al, 2016], a multi-level deep network (MldeNet) is proposed to unify both low-level and high-level information of images.…”

Section: Image Emotion Classificationmentioning

confidence: 99%

“…However, both of the optimization objective functions fail to utilize sentiment ambiguity and similarity information among image categories. [Geng, 2016] proposes a novel machine learning paradigm for describing the exact role of each label, which contains three strategies for the algorithms, i.e. problem transfer (PT), algorithm adaption (AA), and specialized algorithms (SA).…”

Section: Image Emotion Classificationmentioning

confidence: 99%

“…Considering the property of implication, we assign the probability of all other sentiments based on the distance to the dominant label, which indicates that images may evoke kinds of emotional reactions for different people. We generate the distribution l with a univariate Gaussian function, which is widely used in multiple applications [Geng et al, 2013;Geng, 2016]. Hence, the probability density function can be written as follows:…”

Section: Constraint 1: Implicationmentioning

confidence: 99%

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Joint Image Emotion Classification and Distribution Learning via Deep Convolutional Neural Network

Yang

She

Sun

2017

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence

159

110

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Visual sentiment analysis is attracting more and more attention with the increasing tendency to express emotions through visual contents. Recent algorithms in Convolutional Neural Networks (CNNs) considerably advance the emotion classification, which aims to distinguish differences among emotional categories and assigns a single dominant label to each image. However, the task is inherently ambiguous since an image usually evokes multiple emotions and its annotation varies from person to person. In this work, we address the problem via label distribution learning and develop a multi-task deep framework by jointly optimizing classification and distribution prediction. While the proposed method prefers to the distribution datasets with annotations of different voters, the majority voting scheme is widely adopted as the ground truth in this area, and few dataset has provided multiple affective labels. Hence, we further exploit two weak forms of prior knowledge, which are expressed as similarity information between labels, to generate emotional distribution for each category. The experiments conducted on both distribution datasets, i.e. Emotion6, Flickr LDL, Twitter LDL, and the largest single label dataset, i.e. Flickr and Instagram, demonstrate the proposed method outperforms the state-of-the-art approaches.

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Section: Image Emotion Classificationmentioning

confidence: 99%

Section: Image Emotion Classificationmentioning

confidence: 99%

Section: Constraint 1: Implicationmentioning

confidence: 99%

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Joint Image Emotion Classification and Distribution Learning via Deep Convolutional Neural Network

Yang

She

Sun

2017

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence

159

110

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“…In label distribution learning (LDL) problems [47], otherwise known as probabilistic class label problems [75], any instance can be described in different degrees by each label. This can be modeled as a discrete distribution over the labels, where the probability of a label given a specific instance is called its degree of description.…”

Section: Label Distribution Learningmentioning

confidence: 99%

“…-LDL. A LDL problem can be reduced to multiclass classification by extracting as many single-label examples as labels for each one of the training instances [47]. These new examples are assigned a class corresponding to each label and weighted according to its degree of description.…”

Section: Problem Transformationmentioning

confidence: 99%

A snapshot on nonstandard supervised learning problems: taxonomy, relationships, problem transformations and algorithm adaptations

Charte

García

et al. 2018

Prog Artif Intell

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Machine learning is a field which studies how machines can alter and adapt their behavior, improving their actions according to the information they are given. This field is subdivided into multiple areas, among which the best known are supervised learning (e.g. classification and regression) and unsupervised learning (e.g. clustering and association rules).Within supervised learning, most studies and research are focused on well known standard tasks, such as binary classification, multiclass classification and regression with one dependent variable. However, there are many other less known problems. These are what we generically call nonstandard supervised learning problems. The literature about them is much more sparse, and each study is directed to a specific task. Therefore, the definitions, relations and applications of this kind of learners are hard to find.The goal of this paper is to provide the reader with a broad view on the distinct variations of nonstandard supervised problems. A comprehensive taxonomy summarizing their traits is proposed. A review of the common approaches followed to accomplish them and their main applications is provided as well.

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Multi‐label enhancement manifold learning algorithm for vehicle video

Tan

Zeng

2021

Concurrency and Computation

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In this article, we propose a new multi‐label enhancement manifold learning algorithm to solve the vehicle video classification problem. Predicting multiple objects in a traffic video image is a challenging problem. Traditional multi‐label classification methods can solve the problem of simultaneous detection of multiple labels, but cannot handle high‐dimensional streaming video data. Our idea is to use label distribution learning (LDL) to enrich the label space and improve label recognition in the original label space. We use the feature function representing the manifold structure to guide the geometric meaning of the label space and transform the local topology from the feature space to the label space. We first build a label distribution learner. Next, use the LDL model for classification. The similarity between the two distributions is measured by Bayesian divergence, and the label distribution is learned through the maximum entropy model and the objective function of this article is established. Finally, an enhanced label model of the manifold space is established to reduce the dimensionality of the feature matrix generated during the training phase, so that the supervised information in the label manifold can be used in the incremental manifold space to improve the accuracy of feature extraction. Compared to the latest multi‐label learning methods, our multi‐label enhancement manifold learning method has advantages in predicting performance.

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Label Distribution Learning

Cited by 539 publications

References 31 publications

Joint Image Emotion Classification and Distribution Learning via Deep Convolutional Neural Network

Joint Image Emotion Classification and Distribution Learning via Deep Convolutional Neural Network

A snapshot on nonstandard supervised learning problems: taxonomy, relationships, problem transformations and algorithm adaptations

Multi‐label enhancement manifold learning algorithm for vehicle video

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