Classification of Natural Flower Videos Through Sequential Keyframe Selection Using SIFT and DCNN

Jyothi, V. K.; Guru, D. S.; Kumar, Y. H. Sharath

doi:10.1007/978-981-13-9181-1_27

Cited by 2 publications

(2 citation statements)

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“…Cai et al [9] and Liu et al [10] used color, texture, and shape feature descriptors to identify 3 and 8 diferent CHMs, respectively. Finally, there are also some prior work on leaves and fowers recognition [14][15][16][17], using techniques such as local binary pattern (LBP) [18], histogram of oriented gradients (HOG) [19], and scale-invariant feature transform (SIFT) [20].…”

Section: Traditional Vision Techniques For Herb Recognitionmentioning

confidence: 99%

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Toward Recognition of Easily Confused TCM Herbs on the Smartphone Using Hierarchical Clustering Convolutional Neural Network

Lan

Tsai

et al. 2023

Evidence-Based Complementary and Alternative Medicine

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Background and Objective. The use of Chinese herbal medicines (CHMs) for treatment plays an important role in traditional Chinese medicine (TCM). However, some herbs are easily confused with the others because their shapes/textures look similar and they could have totally different utilities. Recently, deep learning has attracted great attention for the application of image recognition and could be useful for TCM herb identification. Methods. For recognizing easily-confused TCM herbs on a smartphone, we propose a CHM recognition system using hierarchical clustering convolutional neural networks (HCNNs) based on the affinity propagation clustering method. Results. We implement our system on the smartphone and show recognition accuracy close to 98%, based on a dataset of 65 kinds of herbs (including 12 easy-confused herbs pairs). We also investigate the effect of different parameters (e.g., selection of clustering algorithms for HCNNs, types of smartphone, and number of layers in the neural network) on the system performance. Conclusions. In this work, we proposed a hierarchical clustering convolutional neural network (HCNN) method to distinguish similar TCM herbs with a high accuracy. We also showed the usefulness of applying the data augmentation techniques when implementing the proposed system for a variety of smartphones.

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Section: Traditional Vision Techniques For Herb Recognitionmentioning

confidence: 99%

“…It clearly shows that the model can quickly converge with much higher accuracy when the fne-tune option is enabled. [19], LBP [18], and BOW SIFT [20], with CNN. For HOG and LBP implementation, the cell size is set to 32.…”

Section: Chm Datasetmentioning

confidence: 99%