249 citations · 1.2k across the 77 of their papers we have counts for
17 papers · 1 filter
Text-to-image synthesis method evaluation based on visual patterns
William Lund Sommer, Alexandros Iosifidis
A commonly used evaluation metric for text-to-image synthesis is the Inception score (IS) \cite{inceptionscore}, which has been shown to be a quality metric that correlates well wi…
INTEL-TAU: A Color Constancy Dataset
Firas Laakom, Jenni Raitoharju, Alexandros Iosifidis +2
In this paper, we describe a new large dataset for illumination estimation. This dataset, called INTEL-TAU, contains 7022 images in total, which makes it the largest available high…
Null Space Analysis for Class-Specific Discriminant Learning
Jenni Raitoharju, Alexandros Iosifidis
In this paper, we carry out null space analysis for Class-Specific Discriminant Analysis (CSDA) and formulate a number of solutions based on the analysis. We analyze both theoretic…
Mid-price Prediction Based on Machine Learning Methods with Technical and Quantitative Indicators
Adamantios Ntakaris, Juho Kanniainen, Moncef Gabbouj +1
Stock price prediction is a challenging task, but machine learning methods have recently been used successfully for this purpose. In this paper, we extract over 270 hand-crafted fe…
Bag of Color Features For Color Constancy
Firas Laakom, Nikolaos Passalis, Jenni Raitoharju +4
In this paper, we propose a novel color constancy approach, called Bag of Color Features (BoCF), building upon Bag-of-Features pooling. The proposed method substantially reduces th…
Color Constancy Convolutional Autoencoder
Firas Laakom, Jenni Raitoharju, Alexandros Iosifidis +2
In this paper, we study the importance of pre-training for the generalization capability in the color constancy problem. We propose two novel approaches based on convolutional auto…
Multilinear Compressive Learning
Dat Thanh Tran, Mehmet Yamac, Aysen Degerli +2
Compressive Learning is an emerging topic that combines signal acquisition via compressive sensing and machine learning to perform inference tasks directly on a small number of mea…
Speed-up and multi-view extensions to Subclass Discriminant Analysis
Kateryna Chumachenko, Jenni Raitoharju, Alexandros Iosifidis +1
In this paper, we propose a speed-up approach for subclass discriminant analysis and formulate a novel efficient multi-view solution to it. The speed-up approach is developed based…
Graph-Embedded Multi-layer Kernel Extreme Learning Machine for One-class Classification or (Graph-Embedded Multi-layer Kernel Ridge Regression for One-class Classification)
Chandan Gautam, Aruna Tiwari, M. Tanveer
A brain can detect outlier just by using only normal samples. Similarly, one-class classification (OCC) also uses only normal samples to train the model and trained model can be us…
3D Quantum Cuts for Automatic Segmentation of Porous Media in Tomography Images
Junaid Malik, Serkan Kiranyaz, Riyadh Al-Raoush +7
Binary segmentation of volumetric images of porous media is a crucial step towards gaining a deeper understanding of the factors governing biogeochemical processes at minute scales…
Machine Learning Based Analysis of Finnish World War II Photographers
Kateryna Chumachenko, Anssi Männistö, Alexandros Iosifidis +1
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 Finni…
Multimodal Subspace Support Vector Data Description
Fahad Sohrab, Jenni Raitoharju, Alexandros Iosifidis +1
In this paper, we propose a novel method for projecting data from multiple modalities to a new subspace optimized for one-class classification. The proposed method iteratively tran…
Feature Engineering for Mid-Price Prediction with Deep Learning
Adamantios Ntakaris, Giorgio Mirone, Juho Kanniainen +2
Mid-price movement prediction based on limit order book (LOB) data is a challenging task due to the complexity and dynamics of the LOB. So far, there have been very limited attempt…
Data-driven Neural Architecture Learning For Financial Time-series Forecasting
Dat Thanh Tran, Juho Kanniainen, Moncef Gabbouj +1
Forecasting based on financial time-series is a challenging task since most real-world data exhibits nonstationary property and nonlinear dependencies. In addition, different data…
Operational Neural Networks
Serkan Kiranyaz, Turker Ince, Alexandros Iosifidis +1
Feed-forward, fully-connected Artificial Neural Networks (ANNs) or the so-called Multi-Layer Perceptrons (MLPs) are well-known universal approximators. However, their learning perf…
Deep Adaptive Input Normalization for Time Series Forecasting
Nikolaos Passalis, Anastasios Tefas, Juho Kanniainen +2
Deep Learning (DL) models can be used to tackle time series analysis tasks with great success. However, the performance of DL models can degenerate rapidly if the data are not appr…
Temporal Logistic Neural Bag-of-Features for Financial Time series Forecasting leveraging Limit Order Book Data
Nikolaos Passalis, Anastasios Tefas, Juho Kanniainen +2
Time series forecasting is a crucial component of many important applications, ranging from forecasting the stock markets to energy load prediction. The high-dimensionality, veloci…