Detection and visualization of encoded local features as anatomical predictors in cross-sectional images of Lauraceae

Abstract

This paper describes computer vision-based quantitative microscopy and its application toward better understanding species specificity. An image dataset of the Lauraceae family that consists of nine species across six genera was investigated, and structural features were quantified using encoded local features implemented in a bag-of-features framework. Of the algorithms used for feature detection, the scale-invariant feature transform (SIFT) achieved the best performance in species discrimination. In the bag-of-features framework with the SIFT features, each image is represented by a histogram of codewords. The codewords were further analyzed by mapping them to each image to visualize the corresponding anatomical elements. From this analysis, we were able to classify and quantify the modes of aggregation of different combinations of cell elements based on clustered codewords. An analysis of the term frequency-inverse document frequency weights revealed that blob-based codewords are generally shared by all species, whereas corner-based codewords are more species specific.

Evaluation of image partitioning strategies for preserving spatial information of cross-sectional micrographs in automated wood recognition of Fagaceae

“…The superiority of SIFT has been demonstrated in comparative studies of major local feature extraction algorithms in general image classification [18]. For wood identification, it has been reported that SIFT, which actively detects cell corners, is superior to other local feature algorithms [4]. As parameters of the SIFT algorithm for feature extraction, the number of layers in each octave was set to 3, the contrast and edge thresholds were set to 0.06 and 10, respectively, and the sigma value of the Gaussian applied to the image at octave number 0 was set to 1.6.…”

“…Because the XDD_005 dataset has quite imbalanced classes, the F1 score was used as a metric to evaluate the performance of the established models. The F1 score is the harmonic mean of the precision and recall, and is more appropriate than the accuracy for evaluating models that have been trained using an imbalanced dataset [4].…”

“…In the field of wood science, there is a growing interest in computer vision (CV)-based wood identification. Applications of CV are expanding from automated wood identification systems to wood anatomical approaches [1][2][3][4]. The recent emergence of open wood image databases, such as the forest species database of the Laboratory of Vision, Robotics and Imaging at the Federal University of Parana [5,6] and the Xylarium Digital Database for wood information science and education (XDD) [7], provides an opportunity for the further application of CV technologies in wood science.…”

“…Feature extraction from images is the most important process in determining the performance of CVbased wood identification systems. In the classification of micrograph datasets, several studies have proven that local feature techniques for extracting morphological information about wood cells, represented by the scaleinvariant feature transform (SIFT), are superior to texture features such as the gray-level co-occurrence matrix and local binary patterns [4,8]. Moreover, local features encoded within the bag-of-features (BOF) framework [9] not only allow indirect quantification of anatomical elements, but can also improve classification performance [4,10].…”

“…In the classification of micrograph datasets, several studies have proven that local feature techniques for extracting morphological information about wood cells, represented by the scaleinvariant feature transform (SIFT), are superior to texture features such as the gray-level co-occurrence matrix and local binary patterns [4,8]. Moreover, local features encoded within the bag-of-features (BOF) framework [9] not only allow indirect quantification of anatomical elements, but can also improve classification performance [4,10]. However, the CV-based wood identification strategies using micrograph datasets that have been reported to date have only focused on the morphological characterization of wood cells and their quantification, neglecting the spatial information among the features extracted from an image.…”