Learning-Based Leaf Image Recognition Frameworks

Hsiao, Jou Ken; Li, Kang; Chang, Ching Long; Lin, Chen‐Yuan

doi:10.1007/978-3-319-14654-6_5

Cited by 3 publications

(9 citation statements)

References 31 publications

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Section: Recognition Ratementioning

confidence: 99%

“…Also our proposed algorithm work in the same dataset presented in [1,3,4,5,8]. Table 3 lists the recognition rates obtained by bag-of-words (BoW) presented in [1], sparse coding (SC) presented in [1], probabilistic neural network-Based (PNN) presented in [3], move median centers method (MMC) presented in [4], Proposed-method, respectively. In [1] randomly 30 image is selected in the learning stage for building OSDL using sparse representation for feature extracted using SIFT with length n=128 for each descriptor, which is small dimension and tack long time in computation, while the PNN approach proposed in [9] used 1,800 training images for neural network training also take long time.…”

Section: Recognition Ratementioning

confidence: 99%

“…Recognition has become an important technique in various fields for the past few years. recognition algorithms are popular for many kinds of applications such as face recognition, action recognition and leaf recognition [1]. This research has focused on leaf recognition based on the analysis of leaf images.…”

Section: Introductionmentioning

confidence: 99%

“…A shape-based approach for leaf classification presented in many researches [1][2][3][4][5]. Jou-ken [1] presented a planet identification system based on shape-based using feature extracted from the leaf images by scale invariant feature transform (SIFT) method. Yang et al [2] presented a new approach for plant leaf recognition using contour-based shape descriptor called multi-scale triangular centroid distance (MTCD) and dynamic programming (DP).…”

Section: Introductionmentioning

confidence: 99%

“…Online Sparse Dictionary Learning algorithm (OSDL) is a famous algorithm used to build dictionary learning. Most recent dictionary learning is performing by algorithms such as the online dictionary learning (ODL) [12] which is used in Learning-Based Leaf Image Recognition Frameworks [1]. Another algorithm performing data dictionary is K-singular value decomposition (K-SVD) [13].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Novel Data Dictionary Learning for Leaf Recognition

Ibrahem¹,

El-Latif²,

Reda³

2019

SIPIJ

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Automatic leaf recognition via image processing has been greatly important for a number of professionals, such as botanical taxonomic, environmental protectors, and foresters. Learn an over-complete leaf dictionary is an essential step for leaf image recognition. Big leaf images dimensions and training images number is facing of fast and complete data leaves dictionary. In this work an efficient approach applies to construct over-complete data leaves dictionary to set of big images diminutions based on sparse representation. In the proposed method a new cropped-contour method has used to crop the training image. The experiments are testing using correlation between the sparse representation and data dictionary and with focus on the computing time.

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Section: Recognition Ratementioning

confidence: 99%

Section: Recognition Ratementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Novel Data Dictionary Learning for Leaf Recognition

Ibrahem¹,

El-Latif²,

Reda³

2019

SIPIJ

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A vision-based hybrid approach for identification of Anthurium flower cultivars

Soleimanipour

Chegini

2020

Computers and Electronics in Agriculture

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Discriminant WSRC for Large-Scale Plant Species Recognition

Zhang

Zhu

et al. 2017

Computational Intelligence and Neuroscience

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In sparse representation based classification (SRC) and weighted SRC (WSRC), it is time-consuming to solve the global sparse representation problem. A discriminant WSRC (DWSRC) is proposed for large-scale plant species recognition, including two stages. Firstly, several subdictionaries are constructed by dividing the dataset into several similar classes, and a subdictionary is chosen by the maximum similarity between the test sample and the typical sample of each similar class. Secondly, the weighted sparse representation of the test image is calculated with respect to the chosen subdictionary, and then the leaf category is assigned through the minimum reconstruction error. Different from the traditional SRC and its improved approaches, we sparsely represent the test sample on a subdictionary whose base elements are the training samples of the selected similar class, instead of using the generic overcomplete dictionary on the entire training samples. Thus, the complexity to solving the sparse representation problem is reduced. Moreover, DWSRC is adapted to newly added leaf species without rebuilding the dictionary. Experimental results on the ICL plant leaf database show that the method has low computational complexity and high recognition rate and can be clearly interpreted.

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Learning-Based Leaf Image Recognition Frameworks

Cited by 3 publications

References 31 publications

A Novel Data Dictionary Learning for Leaf Recognition

A Novel Data Dictionary Learning for Leaf Recognition

A vision-based hybrid approach for identification of Anthurium flower cultivars

Discriminant WSRC for Large-Scale Plant Species Recognition

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