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feature visualization of Raman spectrum analysis with deep convolutional neural network

ANALYTICA CHIMICA ACTA 2019年 1087卷 11-19页

作者： Fukuhara, Masashi Fujiwara, Kazuhiko Maruyama, Yoshihiro Itoh, Hiroyasu Hamamatsu Photon KK Cent Res Lab Tsukuba Res Lab Ibaraki Japan

We demonstrate a recognition and feature visualization method that uses a deep convolutional neural network for Raman spectrum analysis. The visualization is achieved by calculating important regions in the spectra from weights in pooling and fully-connected layers. The method is first examined for simple Lorentzian spectra, then applied to the spectra of pharmaceutical compounds and numerically mixed amino acids. We investigate the effects of the size and number of convolution filters on the extracted regions for Raman-peak signals using the Lorentzian spectra. It is confirmed that the Raman peak contributes to the recognition by visualizing the extracted features. A near-zero weight value is obtained at the background level region, which appears to be used for baseline correction. Common component extraction is confirmed by an evaluation of numerically mixed amino acid spectra. High weight values at the common peaks and negative values at the distinctive peaks appear, even though the model is given one-hot vectors as the training labels (without a mix ratio). This proposed method is potentially suitable for applications such as the validation of trained models, ensuring the reliability of common component extraction from compound samples for spectral analysis. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Raman spectrum Convolutional neural networks Spectrum recognition feature visualization

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feature visualization in comic artist classification using deep neural networks

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JOURNAL OF BIG DATA 2019年第1期6卷 1页

作者： Young-Min, Kim Hanyang Univ Grad Sch Technol & Innovat Management Seoul South Korea

Deep neural networks have become a standard framework for image analytics. Besides the traditional applications, such as object classification and detection, the latest studies have started to expand the scope of the applications to include artworks. However, popular art forms, such as comics, have been ignored in this trend. This study investigates visual features for comic classification using deep neural networks. An effective input format for comic classification is first defined, and a convolutional neural network is used to classify comic images into eight different artist categories. Using a publicly available dataset, the trained model obtains a mean F1 score of 84% for the classification. A feature visualization technique is also applied to the trained classifier, to verify the internal visual characteristics that succeed in classification. The experimental result shows that the visualized features are significantly different from those of general object classification. This work represents one of the first attempts to examine the visual characteristics of comics using feature visualization, in terms of comic author classification with deep neural networks.

关键词： Artistic styles Comic classification Convolutional neural networks Deep neural networks feature visualization

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feature visualization for 3D Point Cloud Autoencoders

Feature Visualization for 3D Point Cloud Autoencoders

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International Joint Conference on Neural Networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Rios, Thiago van Stein, Bas Menzel, Stefan Baeck, Thomas Sendhoff, Bernhard Wollstadt, Patricia Honda Res Inst Europe GmbH Carl Legien Str 30 D-63073 Offenbach Germany Leiden Inst Adv Comp Sci LIACS Niels Bohrweg 1 NL-2333 CA Leiden Netherlands

ISBN: (纸本)9781728169262

In order to reduce the dimensionality of 3D point cloud representations, autoencoder architectures generate increasingly abstract, compressed features of the input data. Visualizing these features is central to understanding the learning process, however, while successful visualization techniques exist for neural networks applied to computer vision tasks, similar methods for geometric, especially non-Euclidean, input data are currently lacking. Hence, we propose a first-of-kind method to project the features learned by point cloud autoencoders into a 3D-space augmented with color maps. Our proposal explores the properties of 1D-convolutions, used in state-of-the art point cloud autoencoder architectures to handle the input data, which leads to an intuitive interpretation of the visualized features. Furthermore, we tackle the search for relevant co-activations in the feature space by clustering the input data in the latent space, where we explore the correspondence between network features and geometric characteristics of typical shapes of the clusters. We tested our approach with experiments on a benchmark data set, and with three different configurations of a point cloud autoencoder, where we show that the features learned by the autoencoder correlate with the occupancy of the input space by the training data.

关键词： autoencoder deep learning feature visualization

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Pedestrian recognition in multi-camera networks based on deep transfer learning and feature visualization

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NEUROCOMPUTING 2018年 316卷 166-177页

作者： Wang, Jing-Tao Yan, Guo-Li Wang, Hui-Yan Hua, Jing Harbin Inst Technol Sch Comp Sci & Technol Harbin Heilongjiang Peoples R China Zhejiang Gongshang Univ Sch Comp & Informat Engn Hangzhou Zhejiang Peoples R China

The extensive deployment of surveillance cameras in public places, such as subway stations and shopping malls, necessitates automated visual-data processing approaches to match pedestrians across non-overlapping multiple cameras. However, due to the insufficient number of labeled training samples in real surveillance scene, it is difficult to train an effective deep neural network for cross-camera pedestrian recognition. Moreover, the cross-camera variation in viewpoint, illumination, and background makes the task even more challenging. To address these issues, in this paper we propose to transfer the parameters of a pre-trained network to our target network and then update the parameters adaptively using training samples from the target domain. More importantly, we develop new network structures that are specially tailored for cross-camera pedestrian recognition task, and implement a simple yet effective multi-level feature fusion method that yield more discriminative and robust features for pedestrian recognition. Specifically, rather than conventionally perform classification on the single-level feature of the last feature layer, we instead utilize multi-level feature by associating feature visualization with multi-level feature fusion. As another contribution, we have published our codes and extracted features to facilitate further research. Extensive experiments are conducted on WARD, PRID and MARS datasets, we show that the proposed method consistently outperforms state-of-the-arts. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Convolutional neural network Deep transfer learning feature visualization Pedestrian recognition feature fusion

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Joint motion boundary detection and CNN-based feature visualization for video object segmentation

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NEURAL COMPUTING & APPLICATIONS 2020年第8期32卷 4073-4091页

作者： Kamranian, Zahra Nilchi, Ahmad Reza Naghsh Sadeghian, Hamid Tombari, Federico Navab, Nassir Univ Isfahan Fac Comp Engn Dept Artificial Intelligence Esfahan *** Iran Univ Isfahan Fac Engn Esfahan *** Iran Tech Univ Munich Comp Aided Med Procedures & Augmented Real Munich Germany

This paper presents a video object segmentation method which jointly uses motion boundary and convolutional neural network (CNN)-based class-level maps to carry out the co-segmentation of the frames. The key characteristic of the proposed approach is a combination of those two sources of information to create initial object and background regions. These regions are employed within the co-segmentation energy function. The motion boundary map detects the areas which contain the object movement, and the CNN-based class saliency map determines the regions with more impact on acquiring the correct network classification. The proposed approach can be implemented on unconstrained natural videos which include changes in an object's appearance, rapidly moving background, object deformation in non-rigid moving, rapid camera motion and even the existence of a static object. Experimental results on two challenging datasets (i.e., Davis and SegTrackv2 datasets) demonstrate the competitive performance of the proposed method compared with the state-of-the-art approaches.

关键词： Video object segmentation Class saliency map Co-segmentation Convolutional neural network feature visualization Motion boundary

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A Steel Surface Defect Recognition Algorithm Based on Improved Deep Learning Network Model Using feature visualization and Quality Evaluation

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IEEE ACCESS 2020年 8卷 49885-49895页

作者： Guan, Shengqi Lei, Ming Lu, Hao Xian Polytech Univ Sch Mech & Elect Engn Xian 710048 Peoples R China Xian Polytech Univ Xian Key Lab Modern Intelligent Textile Equipment Xian 710048 Peoples R China

Steel defect detection is used to detect defects on the surface of the steel and to improve the quality of the steel surface. However, traditional image detection algorithms cannot meet the detection requirements because of small defect features and low contrast between background and features about steel surface defect datasets. A novel recognition algorithm for steel surface defects based on improved deep learning network models using feature visualization and quality evaluation is proposed in this paper. Firstly, the VGG19 is used to pre-train the steel surface defect classification task and the corresponding DVGG19 is established to extract the feature images in different layers from defects weight model. Secondly, the SSIM and decision tree are used to evaluate the feature image quality and adjust the parameters and structure of VGG19. On this basis, a new VSD network is obtained and used for the classification of steel surface defects. Comparing with ResNet and VGG19 methods, experiment results show that the proposed method markedly can improve the average accuracy of classification, and the model is able to converge quickly, which can be good for steel surface defect recognition using VSD network model of feature visualization and quality evaluation.

关键词： Steel feature extraction Machine learning Kernel Convolution Neural networks visualization Deep learning DeVGG19 network feature visualization steel defect recognition VSD network

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Dimensionality Reduction Oriented Toward the feature visualization for Ischemia Detection

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IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE 2009年第4期13卷 590-598页

作者： Delgado-Trejos, Edilson Perera-Lluna, Alexandre Vallverdu-Ferrer, Montserrat Caminal-Magrans, Pere Castellanos-Dominguez, German Inst Tecnol Metropolitano Machine Intelligence & Pattern Recognit Grp Res Ctr Medellin Colombia Univ Politecn Cataluna Dept Syst Automat & Ind Informat Engn Biomed Engn Res Ctr ES-08034 Barcelona Spain Ctr Biomed Res Network Bioengn Biomat & Nanomed Zaragoza 50018 Spain Univ Nacl Colombia Dept Elect Elect & Comp Engn Control & Digital Signal Proc Grp Bogota Colombia

An effective data representation methodology on high-dimension feature spaces is presented, which allows a better interpretation of subjacent physiological phenomena (namely, cardiac behavior related to cardiovascular diseases), and is based on search criteria over a feature set resulting in an increase in the detection capability of ischemic pathologies, but also connecting these features with the physiologic representation of the ECG. The proposed dimension reduction scheme consists of three levels: projection, interpretation, and visualization. First, a hybrid algorithm is described that projects the multidimensional data to a lower dimension space, gathering the features that contribute similarly in the meaning of the covariance reconstruction in order to find information of clinical relevance over the initial training space. Next, an algorithm of variable selection is provided that further reduces the dimension, taking into account only the variables that offer greater class separability, and finally, the selected feature set is projected to a 2-D space in order to verify the performance of the suggested dimension reduction algorithm in terms of the discrimination capability for ischemia detection. The ECG recordings used in this study are from the European ST-T database and from the Universidad Nacional de Colombia database. In both cases, over 99% feature reduction was obtained, and classification precision was over 99% using a five-nearest-neighbor classifier (5-NN).

关键词： Dimensionality reduction feature extraction feature selection feature visualization ischemia detection multidimensional analysis relevance wavelet transform (WT)

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Dimensionality Reduction Oriented Toward the feature visualization for Ischemia Detection

Dimensionality Reduction Oriented Toward the Feature Visuali...

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5th IEEE International Special Topic Conference on Information Technology in Biomedicine

关键词： Dimensionality reduction feature extraction feature selection feature visualization ischemia detection multidimensional analysis relevance wavelet transform (WT)

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An Interactive Image feature visualization System for Supporting CBIR Study

An Interactive Image Feature Visualization System for Suppor...

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6th International Conference on Image Analysis and Recognition

作者： Hu, Gang Gao, Qigang Dalhousie Univ Fac Comp Sci Halifax NS B3H 1W5 Canada

ISBN: (纸本)9783642026102

CBIR has been an active topic for more than one decade. Current systems still lack in flexibility and accuracy because of semantic gap between image's feature-level and semantic-level representations. Although many techniques have been developed for automatic or semi-automatic retrieval (e.g. interactive browsing, relevance feedback (RF)), issues about how to find Suitable features and how to measure the image content still remain. It has been a challenging task to choose Sound features for coding image content properly. This requires intensive interactive effort for discovering useful regularities between features and content semantics. In this paper, we present an interactive visualization system for supporting feature investigations. It allows users to choose different features, feature combinations, and representations for testing their impacts on measuring content-semantics. The experiments demonstrate how various perceptual edge features and groupings are interactively handled for retrieval measures. The system can be extended to include more features.

关键词： Content-based image retrieval (CBIR) Image feature extraction feature visualization Generic edge tokens (GETs)

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Prediction of the driver's focus of attention based on feature visualization of a deep autonomous driving model

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KNOWLEDGE-BASED SYSTEMS 2022年 251卷

作者： Huang, Tao Fu, Rui Changan Univ Sch Automobile Xian Peoples R China

Driver focus of attention (DFoA) is a fundamental research problem in human-like autonomous driving systems. However, most existing methods require large amounts of ground-truth DFoA data for training, which are difficult to collect. Inspired by the visual interpretability of neural networks, this study innovatively developed a DFoA prediction method based on the feature visualization of a deep autonomous driving model that does not require ground-truth DFoA data to train. Here, we propose a multimodal spatiotemporal convolutional network with an attention mechanism for DFoA prediction. First, semantic and depth images were generated from RGB video frames to enable the multipath convolutional network to learn the spatiotemporal information of successive images. A parameter-free attention mechanism with 3-D weights was incorporated as an energy function to calculate the importance of each neuron. A graph attention network was used to learn the most driving behavior-relevant semantic context features. The learned features were fused, and a convolutional long short-term memory network (ConvLSTM) was adopted to achieve the evolution of the fused features in successive frames while considering historical scene variation. Finally, a novel feature visualization method was designed to predict the DFoA by visualizing the driving behavior-relevant features. The experimental results demonstrated that the proposed method can accurately predict DFoA. (c) 2022 Published by Elsevier B.V.

关键词： Driver?s attention Attention mechanism Autonomous driving feature visualization Graph attention network

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