A saliency-guided clothing attribute recognition method by fusing salient prior information

2020 ◽  
Author(s):  
Chuanfei Hu ◽  
Kai Chen ◽  
Dong Bo
Keyword(s):  
Prior Information ◽  
Recognition Method ◽  
Attribute Recognition

The Application of Entropy Weight of Attribute Recognition Model in Reservoir Eutrophication Evaluation

2013 ◽  
Vol 477-478 ◽  
pp. 870-873
Author(s):  
Du Wu ◽  
De Shan Tang ◽  
Xing Wang Lu ◽  
Wen Zhong Yu
Keyword(s):  
Weight Distribution ◽  
Guangdong Province ◽  
Element Model ◽  
Recognition Method ◽  
Entropy Weight ◽  
Recognition Model ◽  
Calculation Results ◽  
Attribute Recognition ◽  
Matter Element Model

Subjective factors could affect the weight distribution of each index in evaluation of reservoir eutrophication, so the example used entropy to deal with the weight distribution of each index. Combined attributes recognition method, the writer selected six indicators to build the entropy weight of attribute recognition model about reservoir eutrophication of ten large reservoirs in Guangdong Province. By comparing the calculated results with the results of matter-element model, the calculation results were basically consistent. So entropy weight of attribute recognition model is applicable to the evaluation of the reservoir eutrophication and it can ensure the fairness and reasonableness of weight distribution.

scholarly journals A Novel Radar HRRP Recognition Method with Accelerated T-Distributed Stochastic Neighbor Embedding and Density-Based Clustering

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5112 ◽  
Author(s):  
Hao Wu ◽  
Dahai Dai ◽  
Xuesong Wang
Keyword(s):  
Prior Information ◽  
Signal To Noise Ratio ◽  
Recognition Performance ◽  
Principal Component ◽  
High Dimensional ◽  
Recognition Method ◽  
Range Profile ◽  
Density Based Clustering ◽  
Computation Speed ◽  
High Resolution Range Profile

High-resolution range profile (HRRP) has attracted intensive attention from radar community because it is easy to acquire and analyze. However, most of the conventional algorithms require the prior information of targets, and they cannot process a large number of samples in real time. In this paper, a novel HRRP recognition method is proposed to classify unlabeled samples automatically where the number of categories is unknown. Firstly, with the preprocessing of HRRPs, we adopt principal component analysis (PCA) for dimensionality reduction of data. Afterwards, t-distributed stochastic neighbor embedding (t-SNE) with Barnes–Hut approximation is conducted for the visualization of high-dimensional data. It proves to reduce the dimensionality, which has significantly improved the computation speed. Finally, it is exhibited that the recognition performance with density-based clustering is superior to conventional algorithms under the condition of large azimuth angle ranges and low signal-to-noise ratio (SNR).

scholarly journals PARNet: A Joint Loss Function and Dynamic Weights Network for Pedestrian Semantic Attributes Recognition of Smart Surveillance Image

2019 ◽  
Vol 9 (10) ◽  
pp. 2027 ◽  
Author(s):  
Yong Li ◽  
Guofeng Tong ◽  
Xin Li ◽  
Yuebin Wang ◽  
Bo Zou ◽  
...  
Keyword(s):  
Loss Function ◽  
Practical Significance ◽  
Sigmoid Function ◽  
Recognition Method ◽  
Deep Model ◽  
Outdoor Scenes ◽  
Smart Surveillance ◽  
Attribute Recognition ◽  
Semantic Attributes ◽  
Indoor And Outdoor

The capability for recognizing pedestrian semantic attributes, such as gender, clothes color and other semantic attributes is of practical significance in bank smart surveillance, intelligent transportation and so on. In order to recognize the key multi attributes of pedestrians in indoor and outdoor scenes, this paper proposes a deep network with dynamic weights and joint loss function for pedestrian key attribute recognition. First, a new multi-label and multi-attribute pedestrian dataset, which is named NEU-dataset, is built. Second, we propose a new deep model based on DeepMAR model. The new network develops a loss function, which joins the sigmoid function and the softmax loss to solve the multi-label and multi-attribute problem. Furthermore, the dynamic weight in the loss function is adopted to solve the unbalanced samples problem. The experiment results show that the new attribute recognition method has good generalization performance.

scholarly journals Research on radar emitter attribute recognition method

2006 ◽  
Vol 9 (1) ◽  
pp. 45-48
Author(s):  
Guan Xin ◽  
Yi Xiao ◽  
He You
Keyword(s):  
Recognition Method ◽  
Attribute Recognition

scholarly journals Uncertainty Analysis on Risk Assessment of Water Inrush in Karst Tunnels

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Yiqing Hao ◽  
Xiaoli Rong ◽  
Linjian Ma ◽  
Pengxian Fan ◽  
Hao Lu
Keyword(s):  
Monte Carlo ◽  
Probability Distributions ◽  
Water Inrush ◽  
Evaluation Index ◽  
Risk Level ◽  
Random Numbers ◽  
Recognition Method ◽  
Attribute Measure ◽  
Attribute Recognition ◽  
Complex Geology

An improved attribute recognition method is reviewed and discussed to evaluate the risk of water inrush in karst tunnels. Due to the complex geology and hydrogeology, the methodology discusses the uncertainties related to the evaluation index and attribute measure. The uncertainties can be described by probability distributions. The values of evaluation index and attribute measure were employed through random numbers generated by Monte Carlo simulations and an attribute measure belt was chosen instead of the linearity attribute measure function. Considering the uncertainties of evaluation index and attribute measure, the probability distributions of four risk grades are calculated using random numbers generated by Monte Carlo simulation. According to the probability distribution, the risk level can be analyzed under different confidence coefficients. The method improvement is more accurate and feasible compared with the results derived from the attribute recognition model. Finally, the improved attribute recognition method was applied and verified in Longmenshan tunnel in China.

An Image Reconstruction System Applied to High Voltage Microscopy

1975 ◽  
Vol 33 ◽  
pp. 292-293
Author(s):  
D. E. Johnson
Keyword(s):  
High Voltage ◽  
Prior Information ◽  
Block Diagram ◽  
Three Dimensional ◽  
Real Space ◽  
Two Dimensional ◽  
Efficient Manner ◽  
Fourier Methods ◽  
Tilt Series ◽  
Methods Of Reconstruction

Increased specimen penetration; the principle advantage of high voltage microscopy, is accompanied by an increased need to utilize information on three dimensional specimen structure available in the form of two dimensional projections (i.e. micrographs). We are engaged in a program to develop methods which allow the maximum use of information contained in a through tilt series of micrographs to determine three dimensional speciman structure.In general, we are dealing with structures lacking in symmetry and with projections available from only a limited span of angles (±60°). For these reasons, we must make maximum use of any prior information available about the specimen. To do this in the most efficient manner, we have concentrated on iterative, real space methods rather than Fourier methods of reconstruction. The particular iterative algorithm we have developed is given in detail in ref. 3. A block diagram of the complete reconstruction system is shown in fig. 1.

Sign in / Sign up

Export Citation Format

Share Document