Features extraction techniques for pollen grain classification

Pozo-Baños, Marcos del; Ticay-Rivas, Jaime R.; Alonso, Jesús B.; Travieso, Carlos M.

doi:10.1016/j.neucom.2014.05.085

Cited by 14 publications

(9 citation statements)

References 29 publications

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“…Identification of pollen grains by image analysis can be performed using various descriptors. Basic shape characteristics are commonly used [ 4 , 6 ], nevertheless, other characteristics resulting from the textural properties of pollen grains can be utilized as well [ 19 ]. For applications other than mellisopalynology, the benefits of EDF for improving discrimination of the objects studied were also confirmed [ 31 ].…”

Section: Resultsmentioning

confidence: 99%

“…A number of different microscopic techniques are used for pollen identification. Morphological characteristics [ 3 ], especially the shape characteristics (length, width, circularity, and shape factor or length/width ratio) [ 4 , 5 ], are used most commonly. In light microscopy (LM) the morphological (pollen shape) and surface structures (exina) are used for the identification as well [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

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Identification of pollen taxa by different microscopy techniques

et al. 2021

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Melissopalynology is an important analytical method to identify botanical origin of honey. Pollen grain recognition is commonly performed by visual inspection by a trained person. An alternative method for visual inspection is automated pollen analysis based on the image analysis technique. Image analysis transfers visual information to mathematical descriptions. In this work, the suitability of three microscopic techniques for automatic analysis of pollen grains was studied. 2D and 3D morphological characteristics, textural and colour features, and extended depth of focus characteristics were used for the pollen discrimination. In this study, 7 botanical taxa and a total of 2482 pollen grains were evaluated. The highest correct classification rate of 93.05% was achieved using the phase contrast microscopy, followed by the dark field microscopy reaching 91.02%, and finally by the light field microscopy reaching 88.88%. The most significant discriminant characteristics were morphological (2D and 3D) and colour characteristics. Our results confirm the potential of using automatic pollen analysis to discriminate pollen taxa in honey. This work provides the basis for further research where the taxa dataset will be increased, and new descriptors will be studied.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification of pollen taxa by different microscopy techniques

et al. 2021

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“…Other types of classifiers have been used less frequently, e.g. the nearest neighbor method ( Chen et al, 2006 ; Bonton et al, 2001 ; Boucher et al, 2002 ), bayes classifiers ( Ranzato et al, 2007 ; Tello-Mijares and Flores, 2016 ), the support vector method (SVM) ( Marcos et al, 2015 ; del Pozo-Baños et al, 2015 ), and random forest ( Tello-Mijares and Flores, 2016 ).…”

Section: Discussionmentioning

confidence: 99%

“…In determining the discriminative features, we directly link them to the specific taxa and at the same time obtain a high percentage of correct classification. One of the latest works describing a feature selection before pollen classification is the paper where dimensionality reduction is made using Linear Discriminant Analysis ( del Pozo-Baños et al, 2015 ). However, the attribute selection method, given by the above-mentioned authors, differs from ours, since it generates a space of new synthetic variables with a different interpretation, whereas in our case we leave the subset of the most strongly discriminating original features.…”

Section: Discussionmentioning

confidence: 99%

Selection of morphological features of pollen grains for chosen tree taxa

2018

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The basis of aerobiological studies is to monitor airborne pollen concentrations and pollen season timing. This task is performed by appropriately trained staff and is difficult and time consuming. The goal of this research is to select morphological characteristics of grains that are the most discriminative for distinguishing between birch, hazel and alder taxa and are easy to determine automatically from microscope images. This selection is based on the split attributes of the J4.8 classification trees built for different subsets of features. Determining the discriminative features by this method, we provide specific rules for distinguishing between individual taxa, at the same time obtaining a high percentage of correct classification. The most discriminative among the 13 morphological characteristics studied are the following: number of pores, maximum axis, minimum axis, axes difference, maximum oncus width, and number of lateral pores. The classification result of the tree based on this subset is better than the one built on the whole feature set and it is almost 94%. Therefore, selection of attributes before tree building is recommended. The classification results for the features easiest to obtain from the image, i.e. maximum axis, minimum axis, axes difference, and number of lateral pores, are only 2.09 pp lower than those obtained for the complete set, but 3.23 pp lower than the results obtained for the selected most discriminating attributes only.

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“…Identification of phytolith morphotypes that have surface ornamentation, which is currently defined qualitatively (ICPN 2005), may benefit from the quantitative description of texture. A number of mathematical measures of texture have been developed (Nixon and Aguado 2002) and applied to discrimination of objects, such as pollen, that rely on texture as a major distinguishing feature (Mander et al 2013;Marcos and Cristobal 2013;Marcos et al 2015;Pozo-Banos et al 2015;Redondo et al 2015).…”

Section: Surface Texture Descriptorsmentioning

confidence: 99%

A conceptual framework for a computer-assisted, morphometric-based phytolith analysis and classification system

Evett

Cuthrell

2016

Journal of Archaeological Science

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Although automated approaches to shape analysis and object classification have been widely applied in the biological sciences, technical and time considerations have limited their use in phytolith research. As advanced microscopy systems become more affordable and accessible and digital imaging software provides a wider range of sophisticated analytical tools, there is increased potential for effective use of machinevision and automation in phytolith research. In this paper, we describe technical limitations of phytolith imaging and identify several techniques that might improve results. Drawing on examples of software developed for related disciplines, we then describe a conceptual framework for development and integration of automated phytolith analysis software for: separating phytoliths from non-phytolith material in digital images; segmentation of phytolith boundaries; quantitative phytolith feature extraction, including a discussion of potentially more powerful, non-traditional parameters of phytolith shape and texture; phytolith classification and identification; and phytolith database image retrieval. While recognizing the difficulty of implementing this framework and the need for extensive empirical testing of suggested approaches on phytoliths, we examine the possibility of aggregating quantitative phytolith data collected in studies worldwide to construct a cloud-based database of phytolith images with associated morphotype data.

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Features extraction techniques for pollen grain classification

Cited by 14 publications

References 29 publications

Identification of pollen taxa by different microscopy techniques

Identification of pollen taxa by different microscopy techniques

Selection of morphological features of pollen grains for chosen tree taxa

A conceptual framework for a computer-assisted, morphometric-based phytolith analysis and classification system

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