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Wyszukujesz frazę "image classifier" wg kryterium: Temat


Wyświetlanie 1-2 z 2
Tytuł:
Could k-NN classifier be Useful in tree leaves recognition?
Autorzy:
Horaisová, K.
Powiązania:
https://bibliotekanauki.pl/articles/229900.pdf
Data publikacji:
2014
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
binary image
Fourier transform
affine invariance
harmonic analysis
pattern recognition
k-NN classifier
Opis:
This paper presents a method for affine invariant recognition of two-dimensional binary objects based on 2D Fourier power spectrum. Such function is translation invariant and their moments of second order enable construction of affine invariant spectrum except of the rotation effect. Harmonic analysis of samples on circular paths generates Fourier coefficients whose absolute values are affine invariant descriptors. Affine invariancy is approximately saved also for large digital binary images as demonstrated in the experimental part. The proposed method is tested on artificial data set first and consequently on a large set of 2D binary digital images of tree leaves. High dimensionality of feature vectors is reduced via the kernel PCA technique with Gaussian kernel and the k-NN classifier is used for image classification. The results are summarized as k-NN classifier sensitivity after dimensionality reduction. The resulting descriptors after dimensionality reduction are able to distinguish real contours of tree leaves with acceptable classification error. The general methodology is directly applicable to any set of large binary images. All calculations were performed in the MATLAB environment.
Źródło:
Archives of Control Sciences; 2014, 24, 2; 177-192
1230-2384
Pojawia się w:
Archives of Control Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Applying LCS to affective image classification in spatial - frequency domain
Autorzy:
Lee, P. -M.
Hsiao, T.-C.
Powiązania:
https://bibliotekanauki.pl/articles/91808.pdf
Data publikacji:
2014
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
image classification
pattern recognition
Hilbert-Huang transform
HHT
empirical mode decomposition
EMD
Hilbert transform
HT
Extended Classifier Systems
XCSs
Area Under Curve
AUC
Radial-Basis Function Network
RBF Network
LCS
Opis:
Recent studies have utilizes color, texture, and composition information of images to achieve affective image classification. However, the features related to spatial-frequency domain that were proven to be useful for traditional pattern recognition have not been tested in this field yet. Furthermore, the experiments conducted by previous studies are not internationally-comparable due to the experimental paradigm adopted. In addition, contributed by recent advances in methodology, that are, Hilbert-Huang Transform (HHT) (i.e. Empirical Mode Decomposition (EMD) and Hilbert Transform (HT)), the resolution of frequency analysis has been improved. Hence, the goal of this research is to achieve the affective image-classification task by adopting a standard experimental paradigm introduces by psychologists in order to produce international-comparable and reproducible results; and also to explore the affective hidden patterns of images in the spatial-frequency domain. To accomplish these goals, multiple human-subject experiments were conducted in laboratory. Extended Classifier Systems (XCSs) was used for model building because the XCS has been applied to a wide range of classification tasks and proved to be competitive in pattern recognition. To exploit the information in the spatial-frequency domain, the traditional EMD has been extended to a two-dimensional version. To summarize, the model built by using the XCS achieves Area Under Curve (AUC) = 0.91 and accuracy rate over 86%. The result of the XCS was compared with other traditional machine-learning algorithms (e.g., Radial-Basis Function Network (RBF Network)) that are normally used for classification tasks. Contributed by proper selection of features for model building, user-independent findings were obtained. For example, it is found that the horizontal visual stimulations contribute more to the emotion elicitation than the vertical visual stimulation. The effect of hue, saturation, and brightness; is also presented.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2014, 4, 2; 99-123
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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