Writer identification using VLAD encoded contour-Zernike moments

Christlein, Vincent; Bernecker, David; Angelopoulou, Elli

doi:10.1109/icdar.2015.7333893

Cited by 28 publications

(19 citation statements)

References 28 publications

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“…In addition, we encode the all-local descriptors of each test page as the global feature vector with VLAD, which encodes the first-order statistics by aggregating the residuals of local features to their corresponding nearest cluster centroid. VLAD is a standard encoding method and has been widely used in writer identification [5,9] and other information retrieval tasks [4,37]. Formally, a codebook D = {c 1 , c 2 , ..., c k } is first computed by k-means with k centroids, and all S local features f S ∈ R m of every test handwritten image are assigned to their nearest cluster centroid.…”

Section: Encodingmentioning

confidence: 99%

Semi-supervised feature learning for improving writer identification

Chen

Wang

Lin

et al. 2019

Information Sciences

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Data augmentation is usually used by supervised learning approaches for offline writer identification, but such approaches require extra training data and potentially lead to overfitting errors. In this study, a semi-supervised feature learning pipeline was proposed to improve the performance of writer identification by training with extra unlabeled data and the original labeled data simultaneously. Specifically, we proposed a weighted label smoothing regularization (WLSR) method for data augmentation, which assigned the weighted uniform label distribution to the extra unlabeled data. The WLSR method could regularize the convolutional neural network (CNN) baseline to allow more discriminative features to be learned to represent the properties of different writing styles. The experimental results on well-known benchmark datasets (ICDAR2013 and CVL) showed that our proposed semisupervised feature learning approach could significantly improve the baseline measurement and perform competitively with existing writer identification approaches. Our findings provide new insights into offline write identification.

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Section: Encodingmentioning

confidence: 99%

Semi-supervised feature learning for improving writer identification

Chen

Wang

Lin

et al. 2019

Information Sciences

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“…In combination with improvements such as whitening [18], intra-normalization [19], or residual normalization [20], VLAD is one of the standard encoding techniques. We successfully employed it for writer identification [21] and it has already been used in combination with deep-learning-based features for the task of classification and retrieval [22]- [24].…”

Section: B Encodingmentioning

confidence: 99%

Encoding CNN Activations for Writer Recognition

Christlein¹,

Maier²

2018

2018 13th IAPR International Workshop on Document Analysis Systems (DAS)

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The encoding of local features is an essential part for writer identification and writer retrieval. While CNN activations have already been used as local features in related works, the encoding of these features has attracted little attention so far. In this work, we compare the established VLAD encoding with triangulation embedding. We further investigate generalized max pooling as an alternative to sum pooling and the impact of decorrelation and Exemplar SVMs. With these techniques, we set new standards on two publicly available datasets (ICDAR13, KHATT).

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“…Conversely, codebook-based descriptors are based on the well-known Bag-of-(Visual)-Words (BoW) principle, i. e., a global descriptor is created by encoding local descriptors using statistics obtained by a pre-trained dictionary. Fisher vectors [16], VLAD [17] or self organizing maps [18] were employed for writer identification and retrieval. Popular local descriptors for writer identification are based on Scale Invariant Feature Transform [10] (SIFT), see [6], [16], [19], [20].…”

Section: Related Workmentioning

confidence: 99%

“…It encodes first order statistics by aggregating the residuals of local descriptors to their corresponding nearest cluster center. VLAD is a standard encoding method, which has already been used for writer identification [17]. It has also successfully been used to encode CNN activation features for classification and retrieval tasks [24], [30].…”

Section: B Encodingmentioning

confidence: 99%

Unsupervised Feature Learning for Writer Identification and Writer Retrieval

Christlein

Gropp

Fiel

et al. 2017

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)

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Deep Convolutional Neural Networks (CNN) have shown great success in supervised classification tasks such as character classification or dating. Deep learning methods typically need a lot of annotated training data, which is not available in many scenarios. In these cases, traditional methods are often better than or equivalent to deep learning methods. In this paper, we propose a simple, yet effective, way to learn CNN activation features in an unsupervised manner. Therefore, we train a deep residual network using surrogate classes. The surrogate classes are created by clustering the training dataset, where each cluster index represents one surrogate class. The activations from the penultimate CNN layer serve as features for subsequent classification tasks. We evaluate the feature representations on two publicly available datasets. The focus lies on the ICDAR17 competition dataset on historical document writer identification (Historical-WI). We show that the activation features trained without supervision are superior to descriptors of state-of-theart writer identification methods. Additionally, we achieve comparable results in the case of handwriting classification using the ICFHR16 competition dataset on historical Latin script types (CLaMM16).

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Writer identification using VLAD encoded contour-Zernike moments

Cited by 28 publications

References 28 publications

Semi-supervised feature learning for improving writer identification

Semi-supervised feature learning for improving writer identification

Encoding CNN Activations for Writer Recognition

Unsupervised Feature Learning for Writer Identification and Writer Retrieval

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