Feature subset selection is an important approach to deal with high-dimensional data. But selecting the best subset of data is NP hard. So most of feature selection methods cannot handle high-dimensional data efficien...
详细信息
Feature subset selection is an important approach to deal with high-dimensional data. But selecting the best subset of data is NP hard. So most of feature selection methods cannot handle high-dimensional data efficiently, or they can only obtain local optimum instead of global optimum. In these cases, when the data consist of both labeled and unlabeled data, semi-supervised feature selection can make full use of data information. In this paper, we introduce a novel semi-supervised feature selection algorithm, which is a filter method based on Fisher-Markov selector, thus ours can achieve global optimum and computational efficiency under certain kernels.
DeepFakes blur the boundaries between reality and forgery, resulting in the collapse of exiting credit system, causing immeasurable consequences for national security and social order. Through analysis of existing fac...
详细信息
The resolution measurement of 3D reconstructed density map in single particle reconstruction is an important and still an open *** this paper,we propose a new protocol to measure the resolution just from the reconstru...
详细信息
ISBN:
(纸本)9781467397155
The resolution measurement of 3D reconstructed density map in single particle reconstruction is an important and still an open *** this paper,we propose a new protocol to measure the resolution just from the reconstructed density *** approach estimates spectral signal-to-noise ratio(SSNR) of 3D reconstructed map by computing the ratio of signal power to noise power in frequency *** power distributions of signal and noise are estimated from structure particle region and surrounding region segmented by applying a mask *** proposed protocol of calculating SSNR,which we term mask-SSNR(mSSNR),is independent of the reconstruction algorithms and can be used for density maps reconstructed with any reconstruction ***,the mSSNR neither needs to split the dataset into halves like the Fourier shell correlation(FSC) approach,nor any original images or intermediate data like other SSNR calculation methods in this *** mSSNR provides a direct calculation of SSNR based on its original definition,and is proven to be a better approach.
In this paper, a sub-dictionary based sparse coding method is proposed for image representation. The novel sparse coding method substitutes a new regularization item for L1-norm in the sparse representation model. The...
详细信息
ISBN:
(纸本)9781509006212
In this paper, a sub-dictionary based sparse coding method is proposed for image representation. The novel sparse coding method substitutes a new regularization item for L1-norm in the sparse representation model. The proposed sparse coding method involves a series of sub-dictionaries. Each sub-dictionary contains all the training samples except for those from one particular category. For the test sample to be represented, all the sub-dictionaries should linearly represent it apart from the one that does not contain samples from that label, and this sub-dictionary is called irrelevant sub-dictionary. This new regularization item restricts the sparsity of each sub-dictionary's residual, and this restriction is helpful for classification. The experimental results demonstrate that the proposed method is superior to the previous related sparse representation based classification.
Fast robotic unloading of piled deformable box-like objects (e.g. box-like sacks), is undoubtedly of great importance to the industry. Existing systems although fast, can only deal with layered, neatly placed configur...
详细信息
Oracle character recognition—an analysis of ancient Chinese inscriptions found on oracle bones—has become a pivotal field intersecting archaeology, paleography, and historical cultural studies. Traditional methods o...
Oracle character recognition—an analysis of ancient Chinese inscriptions found on oracle bones—has become a pivotal field intersecting archaeology, paleography, and historical cultural studies. Traditional methods of oracle character recognition have relied heavily on manual interpretation by experts, which is not only labor-intensive but also limits broader accessibility to the general public. With recent breakthroughs in patternrecognition and deep learning, there is a growing movement towards the automation of oracle character recognition (OrCR), showing considerable promise in tackling the challenges inherent to these ancient scripts. However, a comprehensive understanding of OrCR still remains elusive. Therefore, this paper presents a systematic and structured survey of the current landscape of OrCR research. We commence by identifying and analyzing the key challenges of OrCR. Then, we provide an overview of the primary benchmark datasets and digital resources available for OrCR. A review of contemporary research methodologies follows, in which their respective efficacies, limitations, and applicability to the complex nature of oracle characters are critically highlighted and examined. Additionally, our review extends to ancillary tasks associated with OrCR across diverse disciplines, providing a broad-spectrum analysis of its applications. We conclude with a forward-looking perspective, proposing potential avenues for future investigations that could yield significant advancements in the field.
This paper presents a paddy growth stages classification using MODIS remote sensing images with support vector machines (SVMs). We collected the paddy growth stages data samples from a series of MODIS mages acquired f...
详细信息
This paper presents a paddy growth stages classification using MODIS remote sensing images with support vector machines (SVMs). We collected the paddy growth stages data samples from a series of MODIS mages acquired from March to July 2012 along paddy field area only. The data are collected based on growth stages phenology of paddy using spectral profile which consists of at least 9 classes for growth stages and 2 classes for dominated soil and cloud. We apply SVMs to build a binary classifier for each class with one against all strategy of multiclass approach. One important issue needed to address is unbalanced prior probability that should be solved by each SVM. In this study, we evaluate the effectiveness of balanced branches strategy that is applied to one against all SVMs learning. Our results shows that the balanced branches strategy does improves in average around 10% classification accuracy during training and validation, and in average around 50% during testing.
Interactive object segmentation is widely used for extracting any user-interested objects from natural images. A common problem with many interactive segmentation approaches is that the object segmentation quality is ...
详细信息
Single image rain removal is an important research direction in the field of computer vision. In this paper, the Multi-scale Features Fusion Network (MFFN) is presented for rain removal. MFFN is mainly composed of Mul...
详细信息
Video object (VO) is an important concept in MPEG-4. For objects can be easily manipulated without visible distortion, the copyright protection of video objects becomes an important issue. This paper presents a waterm...
详细信息
暂无评论