Image Classification of Forage Plants in Fabaceae Family Using Scale Invariant Feature Transform Method

2020 ◽  
Author(s):  
Thidarat Pinthong ◽  
Worawut Yimyam ◽  
Narumol Chumuang ◽  
Mahasak Ketcham ◽  
Patiyuth Pramkeaw ◽  
...  
Keyword(s):  
Image Classification ◽  
Scale Invariant Feature Transform ◽  
Scale Invariant ◽  
Transform Method ◽  
Forage Plants ◽  
Invariant Feature ◽  
Feature Transform ◽  
Scale Invariant Feature

scholarly journals Annular Spatial Pyramid Mapping and Feature Fusion-Based Image Coding Representation and Classification

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Mengxi Xu ◽  
Yingshu Lu ◽  
Xiaobin Wu
Keyword(s):  
Image Classification ◽  
Feature Fusion ◽  
Image Feature ◽  
Classification Model ◽  
Scale Invariant Feature Transform ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Feature Transform ◽  
Scale Invariant Feature ◽  
Spatial Pyramid

Conventional image classification models commonly adopt a single feature vector to represent informative contents. However, a single image feature system can hardly extract the entirety of the information contained in images, and traditional encoding methods have a large loss of feature information. Aiming to solve this problem, this paper proposes a feature fusion-based image classification model. This model combines the principal component analysis (PCA) algorithm, processed scale invariant feature transform (P-SIFT) and color naming (CN) features to generate mutually independent image representation factors. At the encoding stage of the scale-invariant feature transform (SIFT) feature, the bag-of-visual-word model (BOVW) is used for feature reconstruction. Simultaneously, in order to introduce the spatial information to our extracted features, the rotation invariant spatial pyramid mapping method is introduced for the P-SIFT and CN feature division and representation. At the stage of feature fusion, we adopt a support vector machine with two kernels (SVM-2K) algorithm, which divides the training process into two stages and finally learns the knowledge from the corresponding kernel matrix for the classification performance improvement. The experiments show that the proposed method can effectively improve the accuracy of image description and the precision of image classification.

scholarly journals Classification of wood knots using artificial neural networks with texture and local feature-based image descriptors

Holzforschung ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Sung-Wook Hwang ◽  
Taekyeong Lee ◽  
Hyunbin Kim ◽  
Hyunwoo Chung ◽  
Jong Gyu Choi ◽  
...  
Keyword(s):  
Local Feature ◽  
Scale Invariant Feature Transform ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Feature Based ◽  
Feature Transform ◽  
Artificial Neural ◽  
Occurrence Matrix ◽  
Scale Invariant Feature

Abstract This paper describes feature-based techniques for wood knot classification. For automated classification of macroscopic wood knot images, models were established using artificial neural networks with texture and local feature descriptors, and the performances of feature extraction algorithms were compared. Classification models trained with texture descriptors, gray-level co-occurrence matrix and local binary pattern, achieved better performance than those trained with local feature descriptors, scale-invariant feature transform and dense scale-invariant feature transform. Hence, it was confirmed that wood knot classification was more appropriate for texture classification rather than an approach based on morphological classification. The gray-level co-occurrence matrix produced the highest F1 score despite representing images with relatively low-dimensional feature vectors. The scale-invariant feature transform algorithm could not detect a sufficient number of features from the knot images; hence, the histogram of oriented gradients and dense scale-invariant feature transform algorithms that describe the entire image were better for wood knot classification. The artificial neural network model provided better classification performance than the support vector machine and k-nearest neighbor models, which suggests the suitability of the nonlinear classification model for wood knot classification.

scholarly journals Computer Aided Classification of Neuroblastoma Histological Images Using Scale Invariant Feature Transform with Feature Encoding

Diagnostics ◽  
2018 ◽  
Vol 8 (3) ◽  
pp. 56 ◽  
Author(s):  
Soheila Gheisari ◽  
Daniel Catchpoole ◽  
Amanda Charlton ◽  
Zsombor Melegh ◽  
Elise Gradhand ◽  
...  
Keyword(s):  
Local Binary Pattern ◽  
Scale Invariant Feature Transform ◽  
Support Vector ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Feature Encoding ◽  
Histological Images ◽  
Feature Transform ◽  
Scale Invariant Feature

Neuroblastoma is the most common extracranial solid malignancy in early childhood. Optimal management of neuroblastoma depends on many factors, including histopathological classification. Although histopathology study is considered the gold standard for classification of neuroblastoma histological images, computers can help to extract many more features some of which may not be recognizable by human eyes. This paper, proposes a combination of Scale Invariant Feature Transform with feature encoding algorithm to extract highly discriminative features. Then, distinctive image features are classified by Support Vector Machine classifier into five clinically relevant classes. The advantage of our model is extracting features which are more robust to scale variation compared to the Patched Completed Local Binary Pattern and Completed Local Binary Pattern methods. We gathered a database of 1043 histologic images of neuroblastic tumours classified into five subtypes. Our approach identified features that outperformed the state-of-the-art on both our neuroblastoma dataset and a benchmark breast cancer dataset. Our method shows promise for classification of neuroblastoma histological images.

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