# Leaf Image Recognition Based on Bag of Features
###### Abstract  
Plants are ubiquitous in human life. Recognizing an unknown plant by its leaf image quickly is a very interesting and challenging research. With the development of image processing and pattern recognition, plant recognition based on image processing has become possible. Bag of features (BOF) is one of the most powerful models for classification, which has been used for many projects and studies. Dual-output pulse-coupled neural network (DPCNN) has shown a good ability for texture features in image processing such as image segmentation. In this paper, a method based on BOF and DPCNN (BOF_DP) is proposed for leaf classification. BOF_DP achieved satisfactory results in many leaf image datasets. As it is hard to get a satisfactory effect on the large dataset by a single feature, a method (BOF_SC) improved from bag of contour fragments is used for shape feature extraction. BOF_DP and LDA (linear discriminant analysis) algorithms are, respectively, employed for textual feature extraction and reducing the feature dimensionality. Finally, both features are used for classification by a linear support vector machine (SVM), and the proposed method obtained higher accuracy on several typical leaf datasets than existing methods. Keywords:feature extraction; shape context; plant recognition; DPCNN; BOF +
Footnote †: journal: Medical  
components and other parameters were compared and evaluated. [PERSON] et al. [2] introduced pattern recognition and computer vision as well as the application of texture features and pattern recognition. However, most leaves have small inter-class color differences, and some leaves have large intra-class color differences. As illumination may be uneven under natural conditions, the color features will affect recognition results. Therefore, the proposed method uses shape and texture features, which are more robust. Both shape and texture features are used for leaf recognition. In 2012, [PERSON] et al. designed a mobile application Leafsnap, where histograms of curvature over scale (HoCS) [3] as a single (shape) feature was employed for plant identification. Other shape features are also used for leaf recognition, such as centroid-contour distance (CCD) [4], aspect ratio [5], Hu invariant moments [6], polar Fourier transform (PFT) [6], inner distance shape context (IDSC) [7], sinuosity coefficients [8], multiscale region transform (MReT) [9], etc. However, some leaves from different kinds of plants are very similar; the shapes of those leaves even cannot be differentiated by the naked eye. Hence, it is reasonable to use both shape feature and texture feature for leaf recognition. The most commonly used texture features contain entropy sequence (EnS) [10], histogram of gradients (HOG) [11], Zernike moments [12], scale invariant feature transform (SIFT) [13; 14], gray-level co-occurrence matrix (GLCM) [15], and local binary patterns (LBP) [15]. [PERSON] et al. [16] proposed a hybrid framework for plant recognition with complicated background. They extracted the block LBP operators as the texture features and calculated the Fourier descriptors as the shape features. [PERSON] et al. [17] combined 11 shape features, 7 statistical features, and 5 vein features for leaf recognition. [PERSON] et al. [18] used Gabor filter and GLCM to model texture feature and used a set of curvelet transform coefficients together with invariant moments to capture shape feature. [PERSON] [19] proposed a new manifold learning method, namely supervised global-locality preserving projection (SGLP), for plant leaf recognition. [PERSON] et al. [20] proposed a novel approach by using the combination of fuzzy-color and edge-texture histogram to recognize fragmented leaf images. Some features based on Gabor filters [21; 22], fractal dimension [23], locality projection analysis (SLPA) [24], kernel based principal component analysis (KPCA) [25], bag of word (BOW) [22; 26] and convolutional neural networks (CNN) [27] are also used for leaf recognition.