Proceedings ArticleDOI
Writer identification and verification using GMM supervectors
Vincent Christlein,David Bernecker,Florian Hönig,Elli Angelopoulou +3 more
- pp 998-1005
TLDR
This paper proposes a new system for offline writer identification and writer verification using GMM supervectors to encode the feature distribution of individual writers and shows that this approach improves the TOP-1 accuracy of the current best ranked methods.Abstract:
This paper proposes a new system for offline writer identification and writer verification. The proposed method uses GMM supervectors to encode the feature distribution of individual writers. Each supervector originates from an individual GMM which has been adapted from a background model via a maximum-a-posteriori step followed by mixing the new statistics with the background model. We show that this approach improves the TOP-1 accuracy of the current best ranked methods evaluated at the ICDAR-2013 competition dataset from 95.1% [13] to 97.1%, and from 97.9% [11] to 99.2% at the CVL dataset, respectively. Additionally, we compare the GMM supervector encoding with other encoding schemes, namely Fisher vectors and Vectors of Locally Aggregated Descriptors.read more
Citations
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Journal ArticleDOI
Writer Identification Using GMM Supervectors and Exemplar-SVMs
TL;DR: A method for robust offline writer identification using RootSIFT descriptors computed densely at the script contours and using Exemplar-SVMs to train a document-specific similarity measure is described.
Book ChapterDOI
Writer Identification and Retrieval Using a Convolutional Neural Network
Stefan Fiel,Robert Sablatnig +1 more
TL;DR: The method presented is using Convolutional Neural Networks CNN to generate a feature vector for each writer, which is then compared with the precalculated feature vectors stored in the database.
Proceedings ArticleDOI
Unsupervised Feature Learning for Writer Identification and Writer Retrieval
TL;DR: In this paper, the authors propose to learn CNN activation features in an unsupervised manner by clustering the training dataset, where each cluster index represents one surrogate class and the activations from the penultimate CNN layer serve as features for subsequent classification tasks.
Journal ArticleDOI
Writer identification using machine learning approaches: a comprehensive review
TL;DR: A comprehensive review of writer identification methods is presented and taxonomy of dataset, feature extraction methods, as well as classification (conventional and deep learning based) for writer identification is provided.
Book ChapterDOI
Offline Writer Identification Using Convolutional Neural Network Activation Features
TL;DR: This work proposes the use of activation features from CNNs as local descriptors for writer identification by means of GMM supervector encoding, which is further improved by normalization with the KL-Kernel.
References
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Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images that can then be used to reliably match objects in diering images.
Journal ArticleDOI
Speaker Verification Using Adapted Gaussian Mixture Models
TL;DR: The major elements of MIT Lincoln Laboratory's Gaussian mixture model (GMM)-based speaker verification system used successfully in several NIST Speaker Recognition Evaluations (SREs) are described.
Book ChapterDOI
Improving the fisher kernel for large-scale image classification
TL;DR: In an evaluation involving hundreds of thousands of training images, it is shown that classifiers learned on Flickr groups perform surprisingly well and that they can complement classifier learned on more carefully annotated datasets.
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