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- Dimitrios Bouzas, Nikolaos Arvanitopoulos, Anastasios Tefas
- IEEE Transactions on Neural Networks and Learning…
- 2015

In this paper, we propose a novel algorithm for dimensionality reduction that uses as a criterion the mutual information (MI) between the transformed data and their corresponding class labels. The MI is a powerful criterion that can be used as a proxy to the Bayes error rate. Furthermore, recent quadratic nonparametric implementations of MI are… (More)

- Nikolaos Arvanitopoulos, Sabine Süsstrunk
- ICFHR
- 2014

We propose a novel algorithm for automatic text line extraction on color and grayscale manuscript pages without prior binarization. Our algorithm is based on seam carving to compute separating seams between text lines. Seam carving is likely to produce seams that move through gaps between neighboring lines, if no information about the text geometry is… (More)

Nowadays, large collections of old historical manuscripts, which contain valuable information about our cultural heritage, exist in libraries around the world. Recently, there has been much interest in their digitization for preservation reasons, since many of the available manuscripts’ quality has deteriorated from exposure to the environment. Digitization… (More)

Error-Correcting Output Codes (ECOC) with subclasses reveal a common way to solve multi-class classification problems. According to this approach, a multiclass problem is decomposed into several binary ones based on the maximization of the mutual information (MI) between the classes and their respective labels. The MI is modelled through the fast quadratic… (More)

- Florian Simond, Nikolaos Arvanitopoulos, Sabine Süsstrunk
- 2015 IEEE International Conference on Image…
- 2015

We investigate the influence of low-level image features for aesthetics prediction. We show that the aesthetic quality of a photography depends on its context. Image features learned from a specific image category are not necessarily the same as features learned from a generic image collection. Experiments conducted on specific image categories show that… (More)

Error Correcting Output Codes reveal an efficient strategy in dealing with multi-class classification problems. According to this technique, a multi-class problem is decomposed into several binary ones. On these created sub-problems we apply binary classifiers and then, by combining the acquired solutions, we are able to solve the initial multiclass… (More)

- Dimitrios Bouzas, Nikolaos Arvanitopoulos, Anastasios Tefas
- The 2010 International Joint Conference on Neural…
- 2010

Error-Correcting Output Codes (ECOC) reveal a common way to model multi-class classification problems. According to this state of the art technique, a multi-class problem is decomposed into several binary ones. Additionally, on the ECOC framework we can apply the subclass technique (sub-ECOC), where by splitting the initial classes of the problem we create… (More)

Error-Correcting Output Codes (ECOCs) reveal a common way to model multi-class classification problems. According to this state of the art technique, a multi-class problem is decomposed into several binary ones. Additionally, on the ECOC framework we can apply the subclasses technique (sub-ECOC), where by splitting the initial classes of the problem we aim… (More)

- Radhakrishna Achanta, Nikolaos Arvanitopoulos, Sabine Süsstrunk
- 2017 IEEE International Conference on Acoustics…
- 2017

It is challenging to complete an image whose 99% pixels are randomly missing. We present a solution to this extreme image completion problem. As opposed to existing techniques, our solution has a computational complexity that is linear in the number of pixels of the full image and is realtime in practice. For comparable quality of reconstruction, our… (More)

In this note, we describe the sequential minimal optimization (SMO) algorithm to solve the soft margin support vector machine (SVM) binary classification problem, which is to be implemented as part of the miniproject. In order to understand our arguments here, the reader is advised to study the SVM chapter of the course notes, in particular the “Solving the… (More)