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Learning graph affinities for spectral graph-based salient object detection
(
Elsevier Ltd
, 2017 , Article)
In this paper, we propose a novel method for learning graph affinities for salient object detection. First, we assume that a graph representation of an image is given with a predetermined connectivity rule and representative ...
Joint K-Means quantization for Approximate Nearest Neighbor Search
(
Institute of Electrical and Electronics Engineers Inc.
, 2016 , Conference Paper)
Recently, Approximate Nearest Neighbor (ANN) Search has become a very popular approach for similarity search on large-scale datasets. In this paper, we propose a novel vector quantization method for ANN, which introduces ...
Salient object segmentation based on linearly combined affinity graphs
(
Institute of Electrical and Electronics Engineers Inc.
, 2016 , Conference Paper)
In this paper, we propose a graph affinity learning method for a recently proposed graph-based salient object detection method, namely Extended Quantum Cuts (EQCut). We exploit the fact that the output of EQCut is ...
Generalized model of biological neural networks: Progressive operational perceptrons
(
Institute of Electrical and Electronics Engineers Inc.
, 2017 , Conference Paper)
Traditional Artificial Neural Networks (ANNs) such as Multi-Layer Perceptrons (MLPs) and Radial Basis Functions (RBFs) were designed to simulate biological neural networks
An optimized k-NN approach for classification on imbalanced datasets with missing data
(
Springer Verlag
, 2016 , Conference Paper)
In this paper, we describe our solution for the machine learning prediction challenge in IDA 2016. For the given problem of 2-class classification on an imbalanced dataset with missing data, we first develop an imputation ...
Learned vs. engineered features for fine-grained classification of aquatic macroinvertebrates
(
Institute of Electrical and Electronics Engineers Inc.
, 2016 , Conference Paper)
Aquatic macroinvertebrate biomonitoring is an efficient way of assessment of slow and subtle anthropogenic changes and their effect on water quality. It is imperative to have reliable identification and counts of the various ...
A vector quantization based k-NN approach for large-scale image classification
(
Institute of Electrical and Electronics Engineers Inc.
, 2017 , Conference Paper)
The k-nearest-neighbour classifiers (k-NN) have been one of the simplest yet most effective approaches to instance based learning problem for image classification. However, with the growth of the size of image datasets and ...
1-D Convolutional Neural Networks for Signal Processing Applications
(
Institute of Electrical and Electronics Engineers Inc.
, 2019 , Conference Paper)
1D Convolutional Neural Networks (CNNs) have recently become the state-of-the-art technique for crucial signal processing applications such as patient-specific ECG classification, structural health monitoring, anomaly ...
The effect of automated taxa identification errors on biological indices
(
Elsevier Ltd
, 2017 , Article)
In benthic macroinvertebrate biomonitoring systems, the target is to determine the status of ecosystems based on several biological indices. To increase cost-efficiency, computer-based taxa identification for image data ...
Convolutional Neural Networks for patient-specific ECG classification
(
IEEE
, 2015 , Conference Paper)
We propose a fast and accurate patient-specific electrocardiogram (ECG) classification and monitoring system using an adaptive implementation of 1D Convolutional Neural Networks (CNNs) that can fuse feature extraction and ...