scholarly journals Brain-inspired Weighted Normalization for CNN Image Classification

2021 ◽  
Author(s):  
Xu Pan ◽  
Elif Kartal ◽  
Luis Gonzalo Sánchez Giraldo ◽  
Odelia Schwartz
Keyword(s):  
Image Classification ◽  
Current Understanding ◽  
Superior Performance ◽  
Statistical Effect ◽  
Classification Tasks ◽  
The Brain

We studied a local normalization paradigm, namely weighted normalization, that better reflects the current understanding of the brain. Specifically, the normalization weight is trainable, and has a more realistic surround pool selection. Weighted normalization outperformed other normalizations in image classification tasks on Cifar10, Imagenet and a customized textured MNIST dataset. The superior performance is more prominent when the CNN is shallow. The good performance of weighted normalization may be related to its statistical effect of gaussianizing the responses.

scholarly journals Intrinsic spatial knowledge about terrestrial ecology favors the tall for judging distance

2016 ◽  
Vol 2 (8) ◽  
pp. e1501070 ◽  
Author(s):  
Liu Zhou ◽  
Teng Leng Ooi ◽  
Zijiang J. He
Keyword(s):  
Target Location ◽  
Spatial Relationship ◽  
Ground Surface ◽  
Spatial Knowledge ◽  
Superior Performance ◽  
Natural Scenes ◽  
Terrestrial Environment ◽  
Optical Images ◽  
Intermediate Distance ◽  
The Brain

Our sense of vision reliably directs and guides our everyday actions, such as reaching and walking. This ability is especially fascinating because the optical images of natural scenes that project into our eyes are insufficient to adequately form a perceptual space. It has been proposed that the brain makes up for this inadequacy by using its intrinsic spatial knowledge. However, it is unclear what constitutes intrinsic spatial knowledge and how it is acquired. We investigated this question and showed evidence of an ecological basis, which uses the statistical spatial relationship between the observer and the terrestrial environment, namely, the ground surface. We found that in dark and reduced-cue environments where intrinsic knowledge has a greater contribution, perceived target location is more accurate when referenced to the ground than to the ceiling. Furthermore, taller observers more accurately localized the target. Superior performance was also observed in the full-cue environment, even when we compensated for the observers’ heights by having the taller observer sit on a chair and the shorter observers stand on a box. Although fascinating, this finding dovetails with the prediction of the ecological hypothesis for intrinsic spatial knowledge. It suggests that an individual’s accumulated lifetime experiences of being tall and his or her constant interactions with ground-based objects not only determine intrinsic spatial knowledge but also endow him or her with an advantage in spatial ability in the intermediate distance range.

scholarly journals Label Rectification Learning through Kernel Extreme Learning Machine

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Qiang Cai ◽  
Fenghai Li ◽  
Yifan Chen ◽  
Haisheng Li ◽  
Jian Cao ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Extreme Learning Machine ◽  
Classification Performance ◽  
Considerable Progress ◽  
Strong Representation ◽  
Kernel Extreme Learning Machine ◽  
Classification Tasks ◽  
Learning Machine

Along with the strong representation of the convolutional neural network (CNN), image classification tasks have achieved considerable progress. However, majority of works focus on designing complicated and redundant architectures for extracting informative features to improve classification performance. In this study, we concentrate on rectifying the incomplete outputs of CNN. To be concrete, we propose an innovative image classification method based on Label Rectification Learning (LRL) through kernel extreme learning machine (KELM). It mainly consists of two steps: (1) preclassification, extracting incomplete labels through a pretrained CNN, and (2) label rectification, rectifying the generated incomplete labels by the KELM to obtain the rectified labels. Experiments conducted on publicly available datasets demonstrate the effectiveness of our method. Notably, our method is extensible which can be easily integrated with off-the-shelf networks for improving performance.

scholarly journals Applying LCS To Affective Image Classification In Spatial-Frequency Domain

2014 ◽  
Vol 4 (2) ◽  
pp. 99-123 ◽  
Author(s):  
Po-Ming Lee ◽  
Tzu-Chien Hsiao
Keyword(s):  
Pattern Recognition ◽  
Spatial Frequency ◽  
Image Classification ◽  
Frequency Domain ◽  
Model Building ◽  
Experimental Paradigm ◽  
Wide Range ◽  
Classification Tasks ◽  
Affective Image Classification ◽  
Spatial Frequency Domain

Abstract Recent studies have utilizes color, texture, and composition information of images to achieve affective image classification. However, the features related to spatial-frequency domain that were proven to be useful for traditional pattern recognition have not been tested in this field yet. Furthermore, the experiments conducted by previous studies are not internationally-comparable due to the experimental paradigm adopted. In addition, contributed by recent advances in methodology, that are, Hilbert-Huang Transform (HHT) (i.e. Empirical Mode Decomposition (EMD) and Hilbert Transform (HT)), the resolution of frequency analysis has been improved. Hence, the goal of this research is to achieve the affective image-classification task by adopting a standard experimental paradigm introduces by psychologists in order to produce international-comparable and reproducible results; and also to explore the affective hidden patterns of images in the spatial-frequency domain. To accomplish these goals, multiple human-subject experiments were conducted in laboratory. Extended Classifier Systems (XCSs) was used for model building because the XCS has been applied to a wide range of classification tasks and proved to be competitive in pattern recognition. To exploit the information in the spatial-frequency domain, the traditional EMD has been extended to a two-dimensional version. To summarize, the model built by using the XCS achieves Area Under Curve (AUC) = 0.91 and accuracy rate over 86%. The result of the XCS was compared with other traditional machine-learning algorithms (e.g., Radial-Basis Function Network (RBF Network)) that are normally used for classification tasks. Contributed by proper selection of features for model building, user-independent findings were obtained. For example, it is found that the horizontal visual stimulations contribute more to the emotion elicitation than the vertical visual stimulation. The effect of hue, saturation, and brightness; is also presented.

scholarly journals Weighted Spatial Pyramid Matching Collaborative Representation for Remote-Sensing-Image Scene Classification

2019 ◽  
Vol 11 (5) ◽  
pp. 518 ◽  
Author(s):  
Bao-Di Liu ◽  
Jie Meng ◽  
Wen-Yang Xie ◽  
Shuai Shao ◽  
Ye Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Information ◽  
Remote Sensing Image ◽  
Superior Performance ◽  
Collaborative Representation ◽  
Scene Classification ◽  
Spatial Pyramid Matching ◽  
Classification Tasks ◽  
Pyramid Matching ◽  
Spatial Pyramid

At present, nonparametric subspace classifiers, such as collaborative representation-based classification (CRC) and sparse representation-based classification (SRC), are widely used in many pattern-classification and -recognition tasks. Meanwhile, the spatial pyramid matching (SPM) scheme, which considers spatial information in representing the image, is efficient for image classification. However, for SPM, the weights to evaluate the representation of different subregions are fixed. In this paper, we first introduce the spatial pyramid matching scheme to remote-sensing (RS)-image scene-classification tasks to improve performance. Then, we propose a weighted spatial pyramid matching collaborative-representation-based classification method, combining the CRC method with the weighted spatial pyramid matching scheme. The proposed method is capable of learning the weights of different subregions in representing an image. Finally, extensive experiments on several benchmark remote-sensing-image datasets were conducted and clearly demonstrate the superior performance of our proposed algorithm when compared with state-of-the-art approaches.

scholarly journals RSI-CB: A Large-Scale Remote Sensing Image Classification Benchmark Using Crowdsourced Data

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1594
Author(s):  
Haifeng Li ◽  
Xin Dou ◽  
Chao Tao ◽  
Zhixiang Wu ◽  
Jie Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Classification ◽  
Large Scale ◽  
Remote Sensing Image ◽  
National Standard ◽  
Natural Image ◽  
Deep Convolutional Neural Networks ◽  
Remote Sensing Image Classification ◽  
Crowdsourced Data ◽  
Classification Tasks

Image classification is a fundamental task in remote sensing image processing. In recent years, deep convolutional neural networks (DCNNs) have experienced significant breakthroughs in natural image recognition. The remote sensing field, however, is still lacking a large-scale benchmark similar to ImageNet. In this paper, we propose a remote sensing image classification benchmark (RSI-CB) based on massive, scalable, and diverse crowdsourced data. Using crowdsourced data, such as Open Street Map (OSM) data, ground objects in remote sensing images can be annotated effectively using points of interest, vector data from OSM, or other crowdsourced data. These annotated images can, then, be used in remote sensing image classification tasks. Based on this method, we construct a worldwide large-scale benchmark for remote sensing image classification. This benchmark has large-scale geographical distribution and large total image number. It contains six categories with 35 sub-classes of more than 24,000 images of size 256 × 256 pixels. This classification system of ground objects is defined according to the national standard of land-use classification in China and is inspired by the hierarchy mechanism of ImageNet. Finally, we conduct numerous experiments to compare RSI-CB with the SAT-4, SAT-6, and UC-Merced data sets. The experiments show that RSI-CB is more suitable as a benchmark for remote sensing image classification tasks than other benchmarks in the big data era and has many potential applications.

Automated brain tumor segmentation from multimodal MRI data based on Tamura texture feature and an ensemble SVM classifier

2019 ◽  
Vol 12 (4) ◽  
pp. 466-480
Author(s):  
Li Na ◽  
Xiong Zhiyong ◽  
Deng Tianqi ◽  
Ren Kai
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Superior Performance ◽  
Support Vector ◽  
Svm Classifier ◽  
Data Set ◽  
Original Solution ◽  
Content Type ◽  
Tumor Region ◽  
The Brain

Purpose The precise segmentation of brain tumors is the most important and crucial step in their diagnosis and treatment. Due to the presence of noise, uneven gray levels, blurred boundaries and edema around the brain tumor region, the brain tumor image has indistinct features in the tumor region, which pose a problem for diagnostics. The paper aims to discuss these issues. Design/methodology/approach In this paper, the authors propose an original solution for segmentation using Tamura Texture and ensemble Support Vector Machine (SVM) structure. In the proposed technique, 124 features of each voxel are extracted, including Tamura texture features and grayscale features. Then, these features are ranked using the SVM-Recursive Feature Elimination method, which is also adopted to optimize the parameters of the Radial Basis Function kernel of SVMs. Finally, the bagging random sampling method is utilized to construct the ensemble SVM classifier based on a weighted voting mechanism to classify the types of voxel. Findings The experiments are conducted over a sample data set to be called BraTS2015. The experiments demonstrate that Tamura texture is very useful in the segmentation of brain tumors, especially the feature of line-likeness. The superior performance of the proposed ensemble SVM classifier is demonstrated by comparison with single SVM classifiers as well as other methods. Originality/value The authors propose an original solution for segmentation using Tamura Texture and ensemble SVM structure.

scholarly journals Inter-Class Angular Loss for Convolutional Neural Networks

2019 ◽  
Vol 33 ◽  
pp. 3894-3901 ◽  
Author(s):  
Le Hui ◽  
Xiang Li ◽  
Chen Gong ◽  
Meng Fang ◽  
Joey Tianyi Zhou ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Deep Neural Networks ◽  
Learning Difficulties ◽  
Feature Space ◽  
Superior Performance ◽  
Strongly Correlated ◽  
Discriminative Ability ◽  
Practical Applications ◽  
Classification Tasks

Convolutional Neural Networks (CNNs) have shown great power in various classification tasks and have achieved remarkable results in practical applications. However, the distinct learning difficulties in discriminating different pairs of classes are largely ignored by the existing networks. For instance, in CIFAR-10 dataset, distinguishing cats from dogs is usually harder than distinguishing horses from ships. By carefully studying the behavior of CNN models in the training process, we observe that the confusion level of two classes is strongly correlated with their angular separability in the feature space. That is, the larger the inter-class angle is, the lower the confusion will be. Based on this observation, we propose a novel loss function dubbed “Inter-Class Angular Loss” (ICAL), which explicitly models the class correlation and can be directly applied to many existing deep networks. By minimizing the proposed ICAL, the networks can effectively discriminate the examples in similar classes by enlarging the angle between their corresponding class vectors. Thorough experimental results on a series of vision and nonvision datasets confirm that ICAL critically improves the discriminative ability of various representative deep neural networks and generates superior performance to the original networks with conventional softmax loss.

Using Fractal and Local Binary Pattern Features for Classification of ECOG Motor Imagery Tasks Obtained from the Right Brain Hemisphere

2016 ◽  
Vol 26 (06) ◽  
pp. 1650022 ◽  
Author(s):  
Fangzhou Xu ◽  
Weidong Zhou ◽  
Yilin Zhen ◽  
Qi Yuan ◽  
Qi Wu
Keyword(s):  
Motor Imagery ◽  
Local Binary Pattern ◽  
Right Hemisphere ◽  
Ordinary Least Squares ◽  
Superior Performance ◽  
Gradient Boosting ◽  
The Right ◽  
The Brain ◽  
Combined Feature

The feature extraction and classification of brain signal is very significant in brain–computer interface (BCI). In this study, we describe an algorithm for motor imagery (MI) classification of electrocorticogram (ECoG)-based BCI. The proposed approach employs multi-resolution fractal measures and local binary pattern (LBP) operators to form a combined feature for characterizing an ECoG epoch recording from the right hemisphere of the brain. A classifier is trained by using the gradient boosting in conjunction with ordinary least squares (OLS) method. The fractal intercept, lacunarity and LBP features are extracted to classify imagined movements of either the left small finger or the tongue. Experimental results on dataset I of BCI competition III demonstrate the superior performance of our method. The cross-validation accuracy and accuracy is 90.6% and 95%, respectively. Furthermore, the low computational burden of this method makes it a promising candidate for real-time BCI systems.

scholarly journals Novel rapid-acting antidepressants: molecular and cellular signaling mechanisms

2017 ◽  
Vol 1 (4) ◽  
Author(s):  
Alexandra M. Thomas ◽  
Ronald S. Duman
Keyword(s):  
Nmda Receptor ◽  
Signaling Pathways ◽  
Neural Circuits ◽  
Cellular Signaling ◽  
Treatment Options ◽  
Time Lag ◽  
Current Understanding ◽  
Signaling Mechanisms ◽  
Research Techniques ◽  
The Brain

Depression is a chronic, debilitating, and common illness. Currently available pharmacotherapies can be helpful but have several major drawbacks, including substantial rates of low or no response and a long therapeutic time lag. In pursuit of better treatment options, recent research has focussed on rapid-acting antidepressants, including the N-methyl-d-aspartate (NMDA) receptor (NMDAR) antagonist ketamine, which affects a range of signaling pathways in ways that are distinct from the mechanisms of typical antidepressants. Because ketamine and similar drugs hold the promise of dramatically improving treatment options for depressed patients, there has been considerable interest in developing new ways to understand how these compounds affect the brain. Here, we review the current understanding of how rapid-acting antidepressants function, including their effects on neuronal signaling pathways and neural circuits, and the research techniques being used to address these questions.

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