Content-Based Feature Extraction: Image Transforms

Das, Rik

doi:10.1201/9780429352928-5

Cited by 1 publication

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“…If the features are not sufficiently relevant and discriminative, even the most powerful machine-learning algorithm cannot achieve a good performance. The discrimination power is gained by descriptors that provide low intra-class variability and high variability between different classes [4]. The image classification task is a challenging one because images are exposed to different illumination conditions, can be affected by noise and can suffer different transformations.…”

Section: Introductionmentioning

confidence: 99%

3D Texture Feature Extraction and Classification Using GLCM and LBP-Based Descriptors

2021

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Lately, 3D imaging techniques have achieved a lot of progress due to recent developments in 3D sensor technologies. This leads to a great interest regarding 3D image feature extraction and classification techniques. As pointed out in literature, one of the most important and discriminative features in images is the textural content. Within this context, we propose a texture feature extraction technique for volumetric images with improved discrimination power. The method could be used in textured volumetric data classification tasks. To achieve this, we fuse two complementary pieces of information, feature vectors derived from Local Binary Patterns (LBP) and the Gray-Level Co-occurrence Matrix-based methods. They provide information regarding the image pattern and the contrast, homogeneity and local anisotropy in the volumetric data, respectively. The performance of the proposed technique was evaluated on a public dataset consisting of volumetric textured images affected by several transformations. The classifiers used are the Support Vector Machine, k-Nearest Neighbours and Random Forest. Our method outperforms other handcrafted 3D or 2D texture feature extraction methods and typical deep-learning networks. The proposed technique improves the discrimination power and achieves promising results even if the number of images per class is relatively small.

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Section: Introductionmentioning

confidence: 99%