Multicriteria-Based Active Discriminative Dictionary Learning for Scene Recognition

Zheng, Caixia; Yi, Yugen; Qi, Miao; Liu, Fucong; Bi, Chuan‐Xing; Wang, Jianzhong; Kong, Jun

doi:10.1109/access.2017.2786672

Cited by 12 publications

(7 citation statements)

References 48 publications

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“…Here, we only considered person detection, while person segmentation [22], face detection with facial expression analysis [54], group-level emotion recognition [60], or age estimation [1] will open more interesting opportunities. Besides objectlevel analysis, scene recognition [63] will help to further characterize photographers.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Based Analysis of Finnish World War II Photographers

et al. 2020

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In this paper, we demonstrate the benefits of using state-of-the-art machine learning methods in the analysis of historical photo archives. Specifically, we analyze prominent Finnish World War II photographers, who have captured high numbers of photographs in the publicly available Finnish Wartime Photograph Archive, which contains 160,000 photographs from Finnish Winter, Continuation, and Lapland Wars captures in 1939-1945. We were able to find some special characteristics for different photographers in terms of their typical photo content and framing (e.g., close-ups vs. overall shots, number of people). Furthermore, we managed to train a neural network that can successfully recognize the photographer from some of the photos, which shows that such photos are indeed characteristic for certain photographers. We further analyzed the similarities and differences between the photographers using the features extracted from the photographer classifier network. We make our annotations and analysis pipeline publicly available, in an effort to introduce this new research problem to the machine learning and computer vision communities and facilitate future research in historical and societal studies over the photo archives.

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Section: Discussionmentioning

confidence: 99%

Machine Learning Based Analysis of Finnish World War II Photographers

et al. 2020

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“…Therefore, how to design an effective method to choose useful samples from the unlabeled data pool is crucial. The quality of the selection strategy determines whether the selected dataset can effectively contain rich information, remove noise data, and represent the whole dataset [ 16 ]. Numerous algorithms have been proposed to find a small informative sample subset so that the model trained on this small subset is comparable to that trained over the whole dataset.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, pool-based and stream-based methods become two popular strategies for active learning. Most of these methods choose one of the two criteria [ 16 ], i.e., representativeness and informativeness, for data analysis and sample selection. Representativeness and informativeness are designed based on the data distribution and the output of classifier, respectively.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Multi-View Active Learning Method Based on a Double-Branch Network

Liu

Zhang

Zheng

et al. 2020

Entropy

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Artificial intelligence is one of the most popular topics in computer science. Convolutional neural network (CNN), which is an important artificial intelligence deep learning model, has been widely used in many fields. However, training a CNN requires a large amount of labeled data to achieve a good performance but labeling data is a time-consuming and laborious work. Since active learning can effectively reduce the labeling effort, we propose a new intelligent active learning method for deep learning, which is called multi-view active learning based on double-branch network (MALDB). Different from most existing active learning methods, our proposed MALDB first integrates two Bayesian convolutional neural networks (BCNNs) with different structures as two branches of a classifier to learn the effective features for each sample. Then, MALDB performs data analysis on unlabeled dataset and queries the useful unlabeled samples based on different characteristics of two branches to iteratively expand the training dataset and improve the performance of classifier. Finally, MALDB combines multiple level information from multiple hidden layers of BCNNs to further improve the stability of sample selection. The experiments are conducted on five extensively used datasets, Fashion-MNIST, Cifar-10, SVHN, Scene-15 and UIUC-Sports, the experimental results demonstrate the validity of our proposed MALDB.

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“…Dictionary learning (DL) is significant for SRC because it can suppress the useless information to promote the representation and discrimination [13]. To learn a discriminative and small-sized dictionary, a substantial amount of methods have been presented [14][15][16], which can be roughly divided into two categories: unsupervised and supervised. Unsupervised DL methods have achieved satisfactory results by minimizing the representation error.…”

Section: Introductionmentioning

confidence: 99%

Jointly Learning the Discriminative Dictionary and Projection for Face Recognition

Zhang

et al. 2020

Mathematical Problems in Engineering

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Recently, dictionary learning has become an active topic. However, the majority of dictionary learning methods directly employs original or predefined handcrafted features to describe the data, which ignores the intrinsic relationship between the dictionary and features. In this study, we present a method called jointly learning the discriminative dictionary and projection (JLDDP) that can simultaneously learn the discriminative dictionary and projection for both image-based and video-based face recognition. The dictionary can realize a tight correspondence between atoms and class labels. Simultaneously, the projection matrix can extract discriminative information from the original samples. Through adopting the Fisher discrimination criterion, the proposed framework enables a better fit between the learned dictionary and projection. With the representation error and coding coefficients, the classification scheme further improves the discriminative ability of our method. An iterative optimization algorithm is proposed, and the convergence is proved mathematically. Extensive experimental results on seven image-based and video-based face databases demonstrate the validity of JLDDP.

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Multicriteria-Based Active Discriminative Dictionary Learning for Scene Recognition

Cited by 12 publications

References 48 publications

Machine Learning Based Analysis of Finnish World War II Photographers

Machine Learning Based Analysis of Finnish World War II Photographers

An Intelligent Multi-View Active Learning Method Based on a Double-Branch Network

Jointly Learning the Discriminative Dictionary and Projection for Face Recognition

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