12,717 research outputs found
Using basic image features for texture classification
Representing texture images statistically as histograms over a discrete vocabulary of local features has proven widely effective for texture classification tasks. Images are described locally by vectors of, for example, responses to some filter bank; and a visual vocabulary is defined as a partition of this descriptor-response space, typically based on clustering. In this paper, we investigate the performance of an approach which represents textures as histograms over a visual vocabulary which is defined geometrically, based on the Basic Image Features of Griffin and Lillholm (Proc. SPIE 6492(09):1-11, 2007), rather than by clustering. BIFs provide a natural mathematical quantisation of a filter-response space into qualitatively distinct types of local image structure. We also extend our approach to deal with intra-class variations in scale. Our algorithm is simple: there is no need for a pre-training step to learn a visual dictionary, as in methods based on clustering, and no tuning of parameters is required to deal with different datasets. We have tested our implementation on three popular and challenging texture datasets and find that it produces consistently good classification results on each, including what we believe to be the best reported for the KTH-TIPS and equal best reported for the UIUCTex databases
Histogram of gradients of Time-Frequency Representations for Audio scene detection
This paper addresses the problem of audio scenes classification and
contributes to the state of the art by proposing a novel feature. We build this
feature by considering histogram of gradients (HOG) of time-frequency
representation of an audio scene. Contrarily to classical audio features like
MFCC, we make the hypothesis that histogram of gradients are able to encode
some relevant informations in a time-frequency {representation:} namely, the
local direction of variation (in time and frequency) of the signal spectral
power. In addition, in order to gain more invariance and robustness, histogram
of gradients are locally pooled. We have evaluated the relevance of {the novel
feature} by comparing its performances with state-of-the-art competitors, on
several datasets, including a novel one that we provide, as part of our
contribution. This dataset, that we make publicly available, involves
classes and contains about minutes of audio scene recording. We thus
believe that it may be the next standard dataset for evaluating audio scene
classification algorithms. Our comparison results clearly show that our
HOG-based features outperform its competitor
Gender-From-Iris or Gender-From-Mascara?
Predicting a person's gender based on the iris texture has been explored by
several researchers. This paper considers several dimensions of experimental
work on this problem, including person-disjoint train and test, and the effect
of cosmetics on eyelash occlusion and imperfect segmentation. We also consider
the use of multi-layer perceptron and convolutional neural networks as
classifiers, comparing the use of data-driven and hand-crafted features. Our
results suggest that the gender-from-iris problem is more difficult than has so
far been appreciated. Estimating accuracy using a mean of N person-disjoint
train and test partitions, and considering the effect of makeup - a combination
of experimental conditions not present in any previous work - we find a much
weaker ability to predict gender-from-iris texture than has been suggested in
previous work
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