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.
Highlights
The traditional plant classification method is mainly realized by artificial recognition, which has the disadvantages of being time-consuming, susceptible to subjective judgment, and low recognition accuracy, far from meeting the requirements for rapid and accurate plant identification
The process of extracting leaf image features by BOF_DPCNN combining Dual-output pulse-coupled neural network (DPCNN) and Bag of features (BOF) model is mainly divided into four stages: preprocessing, acquisition of DPCNN pulse images, low-level feature extraction, and feature coding
BOF_DPCNN: (a)be entropy vectors obtained before by model; (b) codebook obtained by learning features; (c) the locality-constrained linear coding (LLC) coding; and (d) spatial pyramid matching (SPM) for pooling
Summary
The traditional plant classification method is mainly realized by artificial recognition, which has the disadvantages of being time-consuming, susceptible to subjective judgment, and low recognition accuracy, far from meeting the requirements for rapid and accurate plant identification. Many researchers have studied image processing and pattern recognition as well as paid extensive attention to plant recognition They have used images of plant organs (e.g., leaf, flower, fruit, and bark) for plant recognition. In pattern recognition, using shape, texture, and color features for classification has been widely used. The proposed method uses shape and texture features, which are more robust. Fu 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. A new leaf feature called BOF_DP based on dual-output pulse-coupled neural network (DPCNN) and BOF is proposed, and an improved shape context called BOF_SC is used in our plant image recognition system.
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