Dimensionality Reduction and Reconstruction using Mirroring Neural Networks and Object Recognition based on Reduced Dimension Characteristic Vector
College of Engineering, Osmania University（奥斯马尼亚大学工程学院）；Altech Imaging & Computing（阿尔泰克成像与计算公司）；Sreenidhi Institute of Science and Technology（斯雷尼迪科技学院）
Dasika Ratna Deepthi, Sujeet Kuchibhotla, K. Eswaran
备注：
Presented in IEEE International Conference on Advances in Computer Vision and Information Technology (ACVIT-07), Nov. 28-30 2007
发表信息：
IEEE International Conference On Advances in Computer Vision and Information Tech. (IEEE, ACVIT-07), pp. 348 - 353 (2007)
In this paper, we present a Mirroring Neural Network architecture to perform non-linear dimensionality reduction and Object Recognition using a reduced lowdimensional characteristic vector. In addition to dimensionality reduction, the network also reconstructs (mirrors) the original high-dimensional input vector from the reduced low-dimensional data. The Mirroring Neural Network architecture has more number of processing elements (adalines) in the outer layers and the least number of elements in the central layer to form a converging-diverging shape in its configuration. Since this network is able to reconstruct the original image from the output of the innermost layer (which contains all the information about the input pattern), these outputs can be used as object signature to classify patterns. The network is trained to minimize the discrepancy between actual output and the input by back propagating the mean squared error from the output layer to the input layer. After successfully training the network, it can reduce the dimension of input vectors and mirror the patterns fed to it. The Mirroring Neural Network architecture gave very good results on various test patterns.
Automatic Pattern Classification by Unsupervised Learning Using Dimensionality Reduction of Data with Mirroring Neural Networks
College of Engineering, Osmania University（奥斯马尼亚大学工程学院）；Altech Imaging & Computing（阿尔泰克成像与计算公司）；Sreenidhi Institute of Science and Technology（斯雷尼迪科技学院）
Low Cost, High Efficiency: LiDAR Place Recognition in Vineyards with Matryoshka Representation Learning
University Institute for Engineering Research, Miguel Hernández University（工程研究院，米格尔·赫尔内亚大学）；Department of Electronics and Communications, Politecnico di Torino（电子与通信系，托里诺理工学院）
Jiangnan University（江南大学）；University of Surrey（萨里大学）
In image set classification, a considerable progress has been made by representing original image sets on Grassmann manifolds. In order to extend the advantages of the Euclidean based dimensionality reduction methods to the Grassmann Manifold, several methods have been suggested recently which jointly perform dimensionality reduction and metric learning on Grassmann manifold to improve performance. Nevertheless, when applied to complex datasets, the learned features do not exhibit enough discriminatory power. To overcome this problem, we propose a new method named Grassmannian Discriminant Map…
Chongqing Normal University（重庆师范大学）；Chongqing University（重庆大学）
Recently, Transformer is much popular and plays an important role in the fields of Machine Learning (ML), Natural Language Processing (NLP), and Computer Vision (CV), etc. In this paper, based on the Vision Transformer (ViT) model, a new dimensionality reduction (DR) model is proposed, named Transformer-DR. From data visualization, image reconstruction and face recognition, the representation ability of Transformer-DR after dimensionality reduction is studied, and it is compared with some representative DR methods to understand the difference between Transformer-DR and existing DR methods. The…
In convolutional neural network-based character recognition, pooling layers play an important role in dimensionality reduction and deformation compensation. However, their kernel shapes and pooling operations are empirically predetermined; typically, a fixed-size square kernel shape and max pooling operation are used. In this paper, we propose a meta-learning framework for pooling layers. As part of our framework, a parameterized pooling layer is proposed in which the kernel shape and pooling operation are trainable using two parameters, thereby allowing flexible pooling of the input data. We …
When Face Recognition Meets with Deep Learning: an Evaluation of Convolutional Neural Networks for Face Recognition
University of Surrey（萨里大学）；Queen Mary University of London（伦敦玛丽女王大学）；Chinese Academy of Sciences（中国科学院）
深度学习，特别是卷积神经网络（CNN），最近在人脸识别方面取得了可喜的成果。然而，这仍然是一个悬而未决的问题：为什么CNN工作得很好，以及如何设计一个“好”的架构。现有的工作往往集中在报告CNN架构，工作良好的人脸识别，而不是调查的原因。在这项工作中，我们在一个共同的基础上对基于CNN的人脸识别系统（CNN-FRS）进行了广泛的评估，以使我们的工作易于重现。具体来说，我们使用公共数据库LFW（Labeled Faces in the Wild）来训练CNN，这与大多数现有的CNN在私有数据库上训练不同。我们提出了三个CNN架构，这是第一次报告的架构使用LFW数据训练。本文定量地比较了CNN的体系结构，并评估了不同实现选择的效果。我们确定了CNN-FRS的几个有用的属性。例如，学习的特征的维数可以显着降低，而不会对人脸识别精度产生不利影响。此外，传统的度量学习方法，利用CNN学习的功能进行评估。实验表明，CNN-FRS性能良好的两个关键因素是多个CNN的融合和度量学习。为了使我们的工作可复制，源代码和模型将公开提供。
In this work we investigate a novel approach to handle the challenges of face recognition, which includes rotation, scale, occlusion, illumination etc. Here, we have used thermal face images as those are capable to minimize the affect of illumination changes and occlusion due to moustache, beards, adornments etc. The proposed approach registers the training and testing thermal face images in polar coordinate, which is capable to handle complicacies introduced by scaling and rotation. Line features are extracted from thermal polar images and feature vectors are constructed using these line. Fea…
SurgPLAN++: Universal Surgical Phase Localization Network for Online and Offline Inference
Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science and Innovation, Chinese Academy of Sciences（中国科学院香港创新研究院人工智能与机器人中心）；The Chinese University of Hong Kong（香港中文大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；King’s College London（伦敦国王学院）
Chongqing University of Posts and Telecommunications（重庆邮电大学）
Local binary pattern (LBP) as a kind of local feature has shown its simplicity, easy implementation and strong discriminating power in image recognition. Although some LBP variants are specifically investigated for color image recognition, the color information of images is not adequately considered and the curse of dimensionality in classification is easily caused in these methods. In this paper, a color-related local binary pattern (cLBP) which learns the dominant patterns from the decoded LBP is proposed for color images recognition. This paper first proposes a relative similarity space (RS…
Zhejiang University（浙江大学）；Rochester Institute of Technology（罗切斯特理工学院）；ETH Zurich（苏黎世联邦理工学院）
We devise deep nearest centroids (DNC), a conceptually elegant yet surprisingly effective network for large-scale visual recognition, by revisiting Nearest Centroids, one of the most classic and simple classifiers. Current deep models learn the classifier in a fully parametric manner, ignoring the latent data structure and lacking simplicity and explainability. DNC instead conducts nonparametric, case-based reasoning; it utilizes sub-centroids of training samples to describe class distributions and clearly explains the classification as the proximity of test data and the class sub-centroids in…
SAR Target Recognition Using the Multi-aspect-aware Bidirectional LSTM Recurrent Neural Networks
College of Information Science & Technology, Beijing University of Chemical Technology（北京化工大学信息科学与技术学院）；National Research Center for High-Performance Computing Engineering Technology, Sugon Information Industry Co., Ltd（曙光信息产业股份有限公司高性能计算工程技术国家研究中心）；Sichuan Provincial Key Laboratory of Information Coding and Transmission, Southwest Jiaotong University（西南交通大学信息编码与传输四川省重点实验室）；Institute of Electronics, Chinese Academy of Sciences（中国科学院电子学研究所）
Lossless Attention in Convolutional Networks for Facial Expression Recognition in the Wild
National Engineering Research Center for Multimedia Software (NERCMS), Wuhan University（武汉大学多媒体软件国家工程研究中心）；Iflytek Xingzhi Technology Co., Ltd（讯飞行知科技有限公司）
Machine learning techniques have excelled in the automatic semantic analysis of images, reaching human-level performances on challenging benchmarks. Yet, the semantic analysis of videos remains challenging due to the significantly higher dimensionality of the input data, respectively, the significantly higher need for annotated training examples. By studying the automatic recognition of German sign language videos, we demonstrate that on the relatively scarce training data of 2.800 videos, modern deep learning architectures for video analysis (such as ResNeXt) along with transfer learning on l…
Recognizing objects and scenes are two challenging but essential tasks in image understanding. In particular, the use of RGB-D sensors in handling these tasks has emerged as an important area of focus for better visual understanding. Meanwhile, deep neural networks, specifically convolutional neural networks (CNNs), have become widespread and have been applied to many visual tasks by replacing hand-crafted features with effective deep features. However, it is an open problem how to exploit deep features from a multi-layer CNN model effectively. In this paper, we propose a novel two-stage frame…
Université de Sherbrooke（舍布鲁克大学）；Defence Research and Development Canada（加拿大国防研究与发展部）
We propose a novel low-complexity lidar gesture recognition system for mobile robot control robust to gesture variation. Our system uses a modular approach, consisting of a pose estimation module and a gesture classifier. Pose estimates are predicted from lidar scans using a Convolutional Neural Network trained using an existing stereo-based pose estimation system. Gesture classification is accomplished using a Long Short-Term Memory network and uses a sequence of estimated body poses as input to predict a gesture. Breaking down the pipeline into two modules reduces the dimensionality of the i…
Linear discriminant analysis (LDA) is a fundamental method for feature extraction and dimensionality reduction. Despite having many variants, classical LDA has its own importance, as it is a keystone in human knowledge about statistical pattern recognition. For a dataset containing C clusters, the classical solution to LDA extracts at most C-1 features. Here, we introduce a novel solution to classical LDA, called LDA++, that yields C features, each interpretable as measuring similarity to one cluster. This novel solution bridges dimensionality reduction and multiclass classification. Specifica…
Convolutional Neural Network (CNN) provides leverage to extract and fuse features from all layers of its architecture. However, extracting and fusing intermediate features from different layers of CNN structure is still uninvestigated for Human Action Recognition (HAR) using depth and inertial sensors. To get maximum benefit of accessing all the CNN's layers, in this paper, we propose novel Multistage Gated Average Fusion (MGAF) network which extracts and fuses features from all layers of CNN using our novel and computationally efficient Gated Average Fusion (GAF) network, a decisive integral …
