Hierarchical sparse representation for object recognition

2014 ◽  
Vol 2 (1) ◽  
pp. 46-60
Author(s):  
Toru Nakashika ◽  
Takeshi Okumura ◽  
Tetsuya Takiguchi ◽  
Yasuo Ariki
Keyword(s):  
Object Recognition ◽  
Sparse Representation

scholarly journals Image Object Recognition via Deep Feature-Based Adaptive Joint Sparse Representation

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Wang Wei ◽  
Tang Can ◽  
Wang Xin ◽  
Luo Yanhong ◽  
Hu Yongle ◽  
...  
Keyword(s):  
Object Recognition ◽  
Sparse Representation ◽  
Principal Component ◽  
Training Sample ◽  
Classification Framework ◽  
Training Samples ◽  
Joint Sparse Representation ◽  
Feature Based ◽  
Feature Dictionary ◽  
Image Object

An image object recognition approach based on deep features and adaptive weighted joint sparse representation (D-AJSR) is proposed in this paper. D-AJSR is a data-lightweight classification framework, which can classify and recognize objects well with few training samples. In D-AJSR, the convolutional neural network (CNN) is used to extract the deep features of the training samples and test samples. Then, we use the adaptive weighted joint sparse representation to identify the objects, in which the eigenvectors are reconstructed by calculating the contribution weights of each eigenvector. Aiming at the high-dimensional problem of deep features, we use the principal component analysis (PCA) method to reduce the dimensions. Lastly, combined with the joint sparse model, the public features and private features of images are extracted from the training sample feature set so as to construct the joint feature dictionary. Based on the joint feature dictionary, sparse representation-based classifier (SRC) is used to recognize the objects. Experiments on face images and remote sensing images show that D-AJSR is superior to the traditional SRC method and some other advanced methods.

scholarly journals Special Object Recognition Based on Sparse Representation in Multisource Data Fusion Samples

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Changjun Zha
Keyword(s):  
Object Recognition ◽  
Data Fusion ◽  
Sparse Representation ◽  
Detection System ◽  
Principal Component ◽  
Redundant Data ◽  
Limited Power ◽  
Novel Method ◽  
Different Sources ◽  
Better Than

Wireless sensor networks (WSNs) suffer from limited power and large amounts of redundant data. This paper describes a multisource data fusion method for WSNs that can be combined with the characteristics of a profile detection system. First, principal component analysis is used to extract sample features and eliminate redundant information. Feature samples from different sources are then fused using a method of superposition to reduce the amount of data transmitted by the network. Finally, a mathematical model is proposed. On the basis of this model, a novel method of special object recognition based on sparse representation is developed for multisource data fusion samples according to the distribution of nonzero coefficients under an overcomplete dictionary. The experimental results from numerical simulations show that the proposed recognition method can effectively identify special objects in the fusion samples, and the overall performance is better than that of traditional methods.

Multi-class remote sensing object recognition based on discriminative sparse representation

2016 ◽  
Vol 55 (6) ◽  
pp. 1381 ◽  
Author(s):  
Xin Wang ◽  
Siqiu Shen ◽  
Chen Ning ◽  
Fengchen Huang ◽  
Hongmin Gao
Keyword(s):  
Remote Sensing ◽  
Object Recognition ◽  
Sparse Representation

Use It and Still Lose It?

GeroPsych ◽  
2010 ◽  
Vol 23 (3) ◽  
pp. 169-175 ◽  
Author(s):  
Adrian Schwaninger ◽  
Diana Hardmeier ◽  
Judith Riegelnig ◽  
Mike Martin
Keyword(s):  
Object Recognition ◽  
Cognitive Development ◽  
Recognition Test ◽  
Cognitive Aging ◽  
Recognition Task ◽  
Airport Security ◽  
Visual Object ◽  
Long Term Effects ◽  
Object Recognition Test ◽  
X Ray

In recent years, research on cognitive aging increasingly has focused on the cognitive development across middle adulthood. However, little is still known about the long-term effects of intensive job-specific training of fluid intellectual abilities. In this study we examined the effects of age- and job-specific practice of cognitive abilities on detection performance in airport security x-ray screening. In Experiment 1 (N = 308; 24–65 years), we examined performance in the X-ray Object Recognition Test (ORT), a speeded visual object recognition task in which participants have to find dangerous items in x-ray images of passenger bags; and in Experiment 2 (N = 155; 20–61 years) in an on-the-job object recognition test frequently used in baggage screening. Results from both experiments show high performance in older adults and significant negative age correlations that cannot be overcome by more years of job-specific experience. We discuss the implications of our findings for theories of lifespan cognitive development and training concepts.

Sign in / Sign up

Export Citation Format

Share Document