Plant Leaf Recognition Network Based on Feature Learning and Metric Learning

2020 ◽  
pp. 378-386
Author(s):  
Hongwei Yang ◽  
Di Wu ◽  
Changan Yuan ◽  
Xiao Qin ◽  
Hongjie Wu ◽  
...  
Keyword(s):  
Metric Learning ◽  
Feature Learning ◽  
Plant Leaf

Feature Learning Approach for Facial Recognition Using Deep Metric Learning

2020 ◽  
Vol 17 (9) ◽  
pp. 4125-4130
Author(s):  
Gaurav Karkal ◽  
K. Dhanush Reddy ◽  
Kaushik Singh ◽  
Nikith Hosangadi ◽  
Annapurna P. Patil
Keyword(s):  
Neural Network ◽  
Facial Recognition ◽  
Metric Learning ◽  
Feature Learning ◽  
Human Interaction ◽  
Single Image ◽  
Whole Process ◽  
Knn Classifier ◽  
Deep Metric Learning ◽  
Trained Neural Network

Standard deep learning in the context of facial recognition involves inputting a single image and outputting a label for that image. Deep metric learning distinguishes itself by outputting a real valued feature vector instead of a single label. The usage of deep metric learning has revolutionised facial recognition, making it very accurate and reliable. This paper exhibits the accuracy and reliability of the facial recognition model using deep metric learning in the application of an automated attendance system. The paper presents a non-intrusive attendance system which uses the described neural network to recognize faces and record attendance. The system uses the pre-trained neural network to generate embeddings for faces, using a method known as the triple training step, which is described in the paper. These embeddings are generated from a collection of photos per person. After the embeddings are generated, the system is ready to perform facial recognition on sample photos. CNN is used for facial detection in the sample group photos. Once the faces are detected, a KNN classifier is used for recognizing faces. Finally after the faces are recognized, the attendance for each recognized student is marked in the database. Thus, the whole process of attendance was automated without the requirement of human interaction.

scholarly journals Symptom-Based Identification of G-4 Chili Leaf Diseases Based on Rotation Invariant

2021 ◽  
Vol 8 ◽  
Author(s):  
Sufola Das Chagas Silva Araujo ◽  
V. S. Malemath ◽  
K. Meenakshi Sundaram
Keyword(s):  
Feature Learning ◽  
Region Of Interest ◽  
Physical Method ◽  
Image Database ◽  
Features Extraction ◽  
Plant Leaves ◽  
Rotation Invariant ◽  
Plant Leaf ◽  
Plant Image ◽  
Leaf Image

Instinctive detection of infections by carefully inspecting the signs on the plant leaves is an easier and economic way to diagnose different plant leaf diseases. This defines a way in which symptoms of diseased plants are detected utilizing the concept of feature learning (Sulistyo et al., 2020). The physical method of detecting and analyzing diseases takes a lot of time and has chances of making many errors (Sulistyo et al., 2020). So a method has been developed to identify the symptoms by just acquiring the chili plant leaf image. The methodology used involves image database, extracting the region of interest, training and testing images, symptoms/features extraction of the plant image using moments, building of the symptom vector feature dataset, and finding the correlation and similarity between different symptoms of the plant (Sulistyo et al., 2020). This will detect different diseases of the plant.

scholarly journals Semi-Supervised Deep Metric Learning Networks for Classification of Polarimetric SAR Data

2020 ◽  
Vol 12 (10) ◽  
pp. 1593
Author(s):  
Hongying Liu ◽  
Ruyi Luo ◽  
Fanhua Shang ◽  
Xuechun Meng ◽  
Shuiping Gou ◽  
...  
Keyword(s):  
Real World ◽  
Metric Learning ◽  
Feature Learning ◽  
Linear Mapping ◽  
Layer By Layer ◽  
Learning Networks ◽  
Distance Metric ◽  
Linear Projection ◽  
Deep Metric Learning

Recently, classification methods based on deep learning have attained sound results for the classification of Polarimetric synthetic aperture radar (PolSAR) data. However, they generally require a great deal of labeled data to train their models, which limits their potential real-world applications. This paper proposes a novel semi-supervised deep metric learning network (SSDMLN) for feature learning and classification of PolSAR data. Inspired by distance metric learning, we construct a network, which transforms the linear mapping of metric learning into the non-linear projection in the layer-by-layer learning. With the prior knowledge of the sample categories, the network also learns a distance metric under which all pairs of similarly labeled samples are closer and dissimilar samples have larger relative distances. Moreover, we introduce a new manifold regularization to reduce the distance between neighboring samples since they are more likely to be homogeneous. The categorizing is achieved by using a simple classifier. Several experiments on both synthetic and real-world PolSAR data from different sensors are conducted and they demonstrate the effectiveness of SSDMLN with limited labeled samples, and SSDMLN is superior to state-of-the-art methods.

Unsupervised feature learning via prior information‐based similarity metric learning for face verification

2018 ◽  
Vol 12 (8) ◽  
pp. 966-974
Author(s):  
HongZhong Tang ◽  
Xiao Li ◽  
Xiang Wang ◽  
Lizhen Mao ◽  
Ling Zhu
Keyword(s):  
Prior Information ◽  
Metric Learning ◽  
Feature Learning ◽  
Face Verification ◽  
Unsupervised Feature Learning ◽  
Similarity Metric

scholarly journals Visible Thermal Person Re-Identification via Dual-Constrained Top-Ranking

2018 ◽  
Author(s):  
Mang Ye ◽  
Zheng Wang ◽  
Xiangyuan Lan ◽  
Pong C. Yuen
Keyword(s):  
Metric Learning ◽  
Feature Learning ◽  
Superior Performance ◽  
Specific Information ◽  
Night Time ◽  
Feature Representations ◽  
The Arts ◽  
Discriminative Feature ◽  
The Cross ◽  
Ranking Loss

Cross-modality person re-identification between the thermal and visible domains is extremely important for night-time surveillance applications. Existing works in this filed mainly focus on learning sharable feature representations to handle the cross-modality discrepancies. However, besides the cross-modality discrepancy caused by different camera spectrums, visible thermal person re-identification also suffers from large cross-modality and intra-modality variations caused by different camera views and human poses. In this paper, we propose a dual-path network with a novel bi-directional dual-constrained top-ranking loss to learn discriminative feature representations. It is advantageous in two aspects: 1) end-to-end feature learning directly from the data without extra metric learning steps, 2) it simultaneously handles the cross-modality and intra-modality variations to ensure the discriminability of the learnt representations. Meanwhile, identity loss is further incorporated to model the identity-specific information to handle large intra-class variations. Extensive experiments on two datasets demonstrate the superior performance compared to the state-of-the-arts.

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