Automatic forest species recognition based on multiple feature sets

Kapp, Marcelo N.; Bloot, Rodrigo; Cavalin, Paulo Rodrigo; Oliveira, Luiz S.

doi:10.1109/ijcnn.2014.6889750

Cited by 9 publications

(5 citation statements)

References 13 publications

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“…Computing an ensemble of features in these subdivisions resulted in a boosting of the recognition rate to 93.2%. Kapp et al (2012) also used a quadtree-based approach to assess multiple sets of features on the same dataset and reported recognition scores of 95%. Finally, Hafemann et al (2014) compared textural descriptors with classical classifiers to the performance of a Convolutional Neural Network (CNN), which yielded an accuracy of 95% (Table 2).…”

Section: Resultsmentioning

confidence: 99%

Computer-assisted timber identification based on features extracted from microscopic wood sections

Lens

Liang

Guo

et al. 2020

IAWA J.

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Wood anatomy is one of the most important methods for timber identification. However, training wood anatomy experts is time-consuming, while at the same time the number of senior wood anatomists with broad taxonomic expertise is declining. Therefore, we want to explore how a more automated, computer-assisted approach can support accurate wood identification based on microscopic wood anatomy. For our exploratory research, we used an available image dataset that has been applied in several computer vision studies, consisting of 112 — mainly neotropical — tree species representing 20 images of transverse sections for each species. Our study aims to review existing computer vision methods and compare the success of species identification based on (1) several image classifiers based on manually adjusted texture features, and (2) a state-of-the-art approach for image classification based on deep learning, more specifically Convolutional Neural Networks (CNNs). In support of previous studies, a considerable increase of the correct identification is accomplished using deep learning, leading to an accuracy rate up to 95.6%. This remarkably high success rate highlights the fundamental potential of wood anatomy in species identification and motivates us to expand the existing database to an extensive, worldwide reference database with transverse and tangential microscopic images from the most traded timber species and their look-a-likes. This global reference database could serve as a valuable future tool for stakeholders involved in combatting illegal logging and would boost the societal value of wood anatomy along with its collections and experts.

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Section: Resultsmentioning

confidence: 99%

Computer-assisted timber identification based on features extracted from microscopic wood sections

Lens

Liang

Guo

et al. 2020

IAWA J.

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“…As observed, AMLF with global cost reduction can result in a system with about or less than 1/20 of the cost of SLF, but achieving better recognition rates in both datasets. Comparing with other methods that achieved better results on the microscopic dataset, we see that the methods presented in [6] and [7] reach recognition rates that are 4.15 and 2.51 percentage points better than AMLF, respectively, but with corresponding costs that are 31.2 and 60.0 times higher. However, neither of those methods outperform AMLF in the macroscopic dataset.…”

Section: Summary Of the Resultsmentioning

confidence: 78%

“…Research on microscopic imagens is more recent and became more popular with the release of a public database composed of 2240 images of 112 different species (see Section 2.1), which made benchmarking and evaluation easier. Similarly to the literature on macroscopic images, most of the works on forest species using microscopic images use textural representation such as LBP [12], LPQ [12] and their variants [7,11]. CNN also has been proved to be an interesting alternative for microscopic images [6].…”

Section: State Of the Artmentioning

confidence: 99%

Multi-scale Forest Species Recognition Systems for Reduced Cost

Cavalin¹,

Kapp²,

Oliveira³

2017

Preprint

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This work focuses on cost reduction methods for forest species recognition systems. Current state-of-the-art shows that the accuracy of these systems have increased considerably in the past years, but the cost in time to perform the recognition of input samples has also increased proportionally. For this reason, in this work we focus on investigating methods for cost reduction locally (at either feature extraction or classification level individually) and globally (at both levels combined), and evaluate two main aspects: 1) the impact in cost reduction, given the proposed measures for it; and 2) the impact in recognition accuracy. The experimental evaluation conducted on two forest species datasets demonstrated that, with global cost reduction, the cost of the system can be reduced to less than 1/20 and recognition rates that are better than those of the original system can be achieved.

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“…Kapp et al [17] assessed multiple feature sets using a quadtree-based approach and reported recognition rates of 95 and 88 % for the microscopic [25] and macroscopic [34] databases, respectively. Hafemann et al [13] took a different approach, and instead of using textural descriptors, they used the images to train a convolutional neural network (CNN).…”

Section: Introductionmentioning

confidence: 99%

Forest species recognition based on dynamic classifier selection and dissimilarity feature vector representation

Martins

Oliveira

Britto

et al. 2015

Machine Vision and Applications

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Multiple classifiers on the dissimilarity space are proposed to address the problem of forest species recognition from microscopic images. To that end, classical texturebased features such as Gabor filters, local binary patterns (LBP) and local phase quantization (LPQ), as well as two keypoint-based features, the scale-invariant feature transform (SIFT) and the speeded up robust features (SURF), are used to generate a pool of diverse classifiers on the dissimilarity space. A comprehensive set of experiments on a database composed of 2,240 microscopic images from 112 different forest species was used to evaluate the performance of each individual classifier of the generated pool, the combination of all classifiers, and different dynamic selection of classifiers (DSC) methods. The best result (93.03 %) was observed by incorporating probabilistic information in a DSC method based on multiple classifier behavior.

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Automatic forest species recognition based on multiple feature sets

Cited by 9 publications

References 13 publications

Computer-assisted timber identification based on features extracted from microscopic wood sections

Computer-assisted timber identification based on features extracted from microscopic wood sections

Multi-scale Forest Species Recognition Systems for Reduced Cost

Forest species recognition based on dynamic classifier selection and dissimilarity feature vector representation

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