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دسته بندی:
تشخیص الگو - Pattern recognition
سال انتشار:
2019
عنوان انگلیسی مقاله:
A novel neural network based image descriptor for texture classification
ترجمه فارسی عنوان مقاله:
توصیف کننده تصویر مبتنی بر شبکه عصبی برای طبقه بندی بافت
منبع:
Sciencedirect - Elsevier - Physica A, 526 (2019) 120955: doi:10:1016/j:physa:2019:04:191
نویسنده:
Turker Tuncer ∗, Sengul Dogan, Fatih Ertam
چکیده انگلیسی:
Nowadays, image processing and artificial intelligence have become popular science
areas. The one of the major problems of the image processing is texture classification.
Therefore, many methods have been presented about texture classification.
In this article, a new textural feature extraction method is proposed. In this method,
the feed forward neural networks are utilized as a feature extractor. The main purpose
of the proposed method is to show feature extraction capability of the feed forward
neural network. This descriptor consists of 3 x 3 overlapping blocks division, creating
feature extraction network by using row and column pixels of the block, calculating
feature value, normalization and histogram extraction. Firstly, image is divided into 3 x
3 size of overlapping blocks and pixels of each block are selected to create feed forward
networks. To calculate the weights, neighbor pixel values and the signum function are
used together. Tangent hyperbolic function is utilized as activation function in these
networks. PCA (Principle Component Analysis) reduces feature dimensionality and LDA
(Linear Discriminant Analysis) is chosen as classifier. In order to obtain the experimental
results, the commonly used texture datasets were used with variable parameters. These
datasets are UIUC, Outex and USPTex.
The classification accuracies were calculated as 90.82%, 89.62% and 93.83% for these
datasets respectively. The results were compared with the related 16 methods and the
proposed method achieved the best performance among them. The space complexity
of this method also calculated and the cost of the proposed method was given in
the experiments. The computational costs result of this method demonstrates that
the proposed neural network based image descriptor method has low complexity. The
results clearly illustrated that the proposed textural image descriptor extracts distinctive
features with short execution time, has simple mathematical background, is good
discriminator and outperforms.
Keywords: Feed forward textural feature extraction | Texture analysis | Texture recognition | Pattern recognition | Classification
قیمت: رایگان
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