340 research outputs found
Pose Embeddings: A Deep Architecture for Learning to Match Human Poses
We present a method for learning an embedding that places images of humans in
similar poses nearby. This embedding can be used as a direct method of
comparing images based on human pose, avoiding potential challenges of
estimating body joint positions. Pose embedding learning is formulated under a
triplet-based distance criterion. A deep architecture is used to allow learning
of a representation capable of making distinctions between different poses.
Experiments on human pose matching and retrieval from video data demonstrate
the potential of the method
Learning content-based metrics for music similarity
In this abstract, we propose a method to learn application-specific content-based metrics for music similarity using unsupervised feature learning and neighborhood components analysis. Multiple-timescale features extracted from music audio are embedded into a Euclidean metric space, so that the distance between songs reflects their similarity. We evaluated the method on the GTZAN and Magnatagatune datasets
Evaluation of the impact of the indiscernibility relation on the fuzzy-rough nearest neighbours algorithm
Fuzzy rough sets are well-suited for working with vague, imprecise or
uncertain information and have been succesfully applied in real-world
classification problems. One of the prominent representatives of this theory is
fuzzy-rough nearest neighbours (FRNN), a classification algorithm based on the
classical k-nearest neighbours algorithm. The crux of FRNN is the
indiscernibility relation, which measures how similar two elements in the data
set of interest are. In this paper, we investigate the impact of this
indiscernibility relation on the performance of FRNN classification. In
addition to relations based on distance functions and kernels, we also explore
the effect of distance metric learning on FRNN for the first time. Furthermore,
we also introduce an asymmetric, class-specific relation based on the
Mahalanobis distance which uses the correlation within each class, and which
shows a significant improvement over the regular Mahalanobis distance, but is
still beaten by the Manhattan distance. Overall, the Neighbourhood Components
Analysis algorithm is found to be the best performer, trading speed for
accuracy
Short-segment heart sound classification using an ensemble of deep convolutional neural networks
This paper proposes a framework based on deep convolutional neural networks
(CNNs) for automatic heart sound classification using short-segments of
individual heart beats. We design a 1D-CNN that directly learns features from
raw heart-sound signals, and a 2D-CNN that takes inputs of two- dimensional
time-frequency feature maps based on Mel-frequency cepstral coefficients
(MFCC). We further develop a time-frequency CNN ensemble (TF-ECNN) combining
the 1D-CNN and 2D-CNN based on score-level fusion of the class probabilities.
On the large PhysioNet CinC challenge 2016 database, the proposed CNN models
outperformed traditional classifiers based on support vector machine and hidden
Markov models with various hand-crafted time- and frequency-domain features.
Best classification scores with 89.22% accuracy and 89.94% sensitivity were
achieved by the ECNN, and 91.55% specificity and 88.82% modified accuracy by
the 2D-CNN alone on the test set.Comment: 8 pages, 1 figure, conferenc
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