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

Abstract

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.

A white-box analysis on the writer-independent dichotomy transformation applied to offline handwritten signature verification

“…The Dichotomy Transformation has already been used in various contexts, such as: bird species identification Zottesso et al (2018), forest species recognition Martins et al (2015), writer identification Bertolini et al (2016) and also for handwritten signature verification Rivard et al (2013); Eskander et al (2013); Souza et al (2018b).…”

Automated recognition of wood used in traditional Japanese sculptures by texture analysis of their low-resolution computed tomography data

“…As a method for the extraction of image information, which is one of the important processes in image recognition, a texture analysis has been utilized in a variety of fields such as remote sensing and medical imaging. Several authors have also reported attempts of the application of these techniques to wood identification [7][8][9][10][11][12][13][14][15][16][17][18][19], which are particularly active in tropical areas. Tropical timber is an important biological and economical resource in the developing world; thus, wood identification is demanded at trading locations to circulate proper wood in the market as well as to keep illegally logged timber under observation.…”

“…Under such circumstances, an automated wood identification system is highly demanded because the development of human resources is important, but it is not straightforward to train people to have in-depth anatomical knowledge and experience to cover the large diversity of tropical hardwood species. Many studies have mostly used optical micrographs [10,11,14,19] or stereograms [7-9, 12, 13, 15-18], whose resolution was a several micrometers at worst. Under such conditions, the arrangements of vessels and axial parenchyma were clearly recorded and used as image information.…”

“…Several approaches for the extraction of image information were tested, such as the segmentation of specific anatomical features [10,11], the gray-level cooccurrence matrix (GLCM) [7][8][9], Gabor filtering [16], Gabor filtering followed by the GLCM [12], the Coiflet discrete wavelet transform [18], the extended higher local order autocorrelation [15], and local binary patterns [14]. Combinations of multiple extractors were also tested for further improvement in the systems [13,17,19].…”

Texture analysis of stereograms of diffuse-porous hardwood: identification of wood species used in Tripitaka Koreana

“…In fact, many studies of wood image recognition have been conducted in the last decade [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20], which mainly focused on protecting tropical timber in trading locations.…”