Li Shang

Plant Leaf Recognition Using Histograms of Oriented Gradients

Leaves from plants are proved to be a feasible source of information used to identify plant species [1]. In this paper, we present a method to recognize plant leaves employing Histograms of Oriented Gradients (HOG) as the feature descriptor. For better robustness to illumination, shadow, quality degradation, etc., five vital factors of original HOG algorithm are discussed to evaluate the respective effects in different configurations. Experimental results show that this method achieves excellent performance in recognition rate.

Hybrid Deep Learning for Plant Leaves Classification

Recently, deep learning is very popular, it has been applied into many applications, In this paper, a new neural network, hybrid deep learning is introduced, which included AutoEncoder(AE) and convolutional neural network (CNN). This neural network is applied for extracting the features of the plant leaves. In this paper, we proved that hybrid deep learning can extract better features for classification task. We apply the hybrid deep learning to extract features of leaf pictures, and then we classify leaves using those features with SVM, the result suggests that this method is not only better than pure SVM, but also better than pure AE and pure CNN.

Convolutional Neural Network Application on Leaf Classification

Plants are everywhere in our lives, we can classify them by observing their features. But for ordinary people, the species we don’t know are much more than we know. So, for amateurs who are interested in botany, a system which can classify different species of leaves must be very useful, a system like that will also help students recognize the leaves they don’t know. This paper describes a system for leaf classification, which is developed with convolutional neural network technique. Previous researches in leaf identification usually use grayscale images. The main reason is that these samples mostly are green leaves. This system is trained by 1500 leaves to classify 50 kinds of plants. Compared to other research, our net use RGB images for input. And in convolutional neural network, we use PReLU instead of traditional ReLU. The experimental result shows that our method for classification gives accuracy of 94.8 %.

Leaf Classification Utilizing a Convolutional Neural Network with a Structure of Single Connected Layer

Plant plays an important role in human life, so it is necessary to build an automatic system for recognizing plant. Leaf classification has become a research focus for twenty years. In this paper, we propose a single connected layer (SCL) structure adding into the convolutional neural network (CNN). We use this CNN model for plant leaf identification and report the promising results on ICL leaf database. Moreover, we propose some improvement on it to let it perform better. The result shows that our advanced SCL can effectively improve the accuracy of CNN.

Parallel Image Texture Feature Extraction under Hadoop Cloud Platform

With the increasing amount of digital image data, massive image process and feature extraction process have become a time-consuming process. As an excellent mass data processing and storage capacity of the open source cloud platform, Hadoop provides a parallel computing model MapReduce, HDFS distributed file system module. Firstly, we introduced Hadoop platform programming framework and Tamura texture features. And then, the image processing and feature texture feature extraction calculations involved in the process to achieve Hadoop platform. The results which comparison with Matlab platform shows it is less obvious advantage of Hadoop platform in image processing and feature extraction of lower-resolution images, but for image processing and feature extraction of high-resolution images, the time spent in Hadoop platform is greatly reducing, data processing capability the advantages is obvious.

Visual data completion via local sensitive low rank tensor learning

The problems of estimating missing values in visual data appear ubiquitously in computer vision applications including image inpainting, video inpainting, hyperspectral data recovery, and magnetic resonance imaging (MRI) data recovery. Recently, it is shown that tensor completion, which generalizes matrix completion to multiway data of higher order, could accurately estimate the overall data structure and achieves state-of-the-art performance for video completion. However, current tensor-based approaches implicitly assume that the partially observed video is globally low rank, which is too stringent for practical applications where the input video could include multiple heterogeneous episodes and the global correlation between frames is not high. To tackle this problem, we propose a novel local sensitive formulation of tensor learning where we assume instead that the video is inter-correlated in a local manner, leading to a representation of the observed tensor as a weighted sum of low-rank tensors. Computationally, we also design efficient scheme for solving the resulting learning problem based on the alternating direction method of multipliers (ADMM). Our experiments show improvements in prediction accuracy over classical approaches for visual data completion tasks.

Locally Biased Discriminative Clustering Method for Interactive Image Segmentation

Interactive image segmentation is a form of semi-supervised segmentation method by using the user interactive information. It performed well than fully unsupervised segmentation methods. In this paper, we propose a novel interactive image segmentation method, in which a seed vector is used to represent the user scribbles. Then a soft similarity constraint is added to the discriminative clustering model. The soft constraint allows the user to tune the degree which the constraint is satisfied. With respect to the discriminative clustering model, the clustering result is not affected by the assumption to the distribution of the data, and it’s easy to add constraint to the clustering variable. The final optimization problem is convex, so it can reach global optimal solution. The proposed method is evaluated on benchmark dataset BSD dataset, and it performs well than state of art methods both in quantitative and qualitative results.

Implementation of Plant Leaf Recognition System on ARM Tablet Based on Local Ternary Pattern

The Local Binary Pattern (LBP) and its variants is powerful in capturing image features and computational simplicity, However LBP’s sensitivity to noise, particularly in near-uniform image regions has stimulated many transformations of LBP to improve the ability of feature description. The Local Ternary Pattern (LTP) extends the conventional LBP to ternary codes and makes a significant improvement. LTP is more resistant to noise, but no longer strictly invariant to gray-level transformations. In this paper, by adopting the Average Local Gray Level (ALG) to take place of the traditional gray value of the center pixel and taking an auto-adaptive strategy on the selection of the threshold, we propose the Enhanced Local Ternary Pattern (ELTP) to improve the performance of LTP and implement an android application to recognize plant-leaf image and identify the species of the plant.

Plant Leaf Recognition Based on Contourlet Transform and Support Vector Machine

Plants are essential to the balance of nature and in people’s lives as the fundamental provider for food, oxygen and energy. The study of plants is also essential for environmental protection and helping farmers increase the production of food. As a fundamental task in botanical study, plant leaf recognition has been a hot research topic in these years. In this paper, we propose a new method based on contourlet transform and Support Vector Machine (SVM) for leaf recognition. Contourlet Transform is a promising multi-resolution analysis technique, which provides image with a flexible anisotropy and directional expansion. By basing its constructive principle on a non-subsampled pyramid structure and related directional filter banks, contourlet transform decomposes input images into multi-scale factors which also enjoys additional advantages such as shift invariance and computational efficiency. Compared with one-dimensional transforms, such as the Fourier and wavelet transforms, Contourlet Transform can capture the intrinsic geometrical structure. In order to ameliorate the influence of unwanted artefacts such as illumination and translation variations, in this paper, the contourlet transform was firstly applied to extract feature with high discriminative power. Then the extracted features are classified by SVM. The experimental results show that the proposed method has high sensitivity of directionality and can better capture the rich features of natural images such as edges, curves and contours.

Implementation of Leaf Image Recognition System Based on LBP and B/S Framework

Plant identification system is on the basis of the previous, through continuous optimizing all aspects of the algorithm to improve efficiency and accuracy of the algorithm. For feature extraction, since the local binary pattern was proposed in the past decades, it has been widely used in computer vision to describe the feature for image classification such as image recognition, motion detection and medical image analysis. According to accuracy of the descriptor always fluctuates with different samples, some improved pattern of LBP has been presented in papers. Complete Local Binary Pattern (CLBP) is an optimized version which set an additional magnitude value to local differences. This paper shows extensive experiments of implement the LBP derivatives for plants texture identification. Finally realize an online system to identify what kind of the plant image user uploaded based on LBP descriptor.