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Dual Structure Constrained Multimodal feature coding for Social Event Detection from Flickr Data

ACM TRANSACTIONS ON INTERNET TECHNOLOGY 2017年第2期17卷 19-19页

作者： Yang, Zhenguo Li, Qing Lu, Zheng Ma, Yun Gong, Zhiguo Liu, Wenyin Guangdong Univ Technol Sch Comp Sci & Technol Guangzhou Guangdong Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China City Univ Hong Kong Multimedia Software Engn Res Ctr Hong Kong Hong Kong Peoples R China City Univ Hong Kong Sch Creat Media Hong Kong Hong Kong Peoples R China Univ Macau Dept Comp & Informat Sci Zhuhai Peoples R China

In this work, a three-stage social event detection (SED) framework is proposed to discover events from Flickr-like data. First, multiple bipartite graphs are constructed for the heterogeneous feature modalities to achieve fused features. Furthermore, considering the geometrical structures of dictionary and data, a dual structure constrained multimodal feature coding model is designed to learn discriminative feature codes by incorporating corresponding regularization terms into the objective. Finally, clustering models utilizing density or label knowledge and data recovery residual models are devised to discover real-world events. The proposed SED approach achieves the highest performance on the MediaEval 2014 SED dataset.

关键词： Algorithms Performance Human Factors Social multimedia analytics multimedia content analysis multimodal fusion feature coding event detection

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Face recognition using feature coding and nonlocal constraint-based sparse representation classifier

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JOURNAL OF ELECTRONIC IMAGING 2018年第5期27卷 053050-053050页

作者： Liao, Mengmeng Gu, Xiaodong Fudan Univ Dept Elect Engn Shanghai Peoples R China

We propose a face recognition method using distinctive triangle encoding and nonlocal constraint-based sparse representation (TENCSR). With TENCSR, first the pixel values of all images are used as the low-level features. Next, a clustering method is proposed by considering the density distribution of target data, named shared weights support vector data description (SW-SVDD), which makes the obtained decision boundary closer to the optimal. On the basis of SW-SVDD, a more distinctive triangle encoding (MDTE) method is introduced by considering the cluster center information and the size information of cluster, which makes the encoded features more distinctive. Then the high-level features are obtained by encoding those low-level features using MDTE. Meanwhile, a nonlocal constraint-based sparse representation classifier (NC-SRC) is proposed by the biological discovery that dissimilar inputs have dissimilar codes. Finally, those high-level features are classified by the proposed NC-SRC. Experimental results on Georgia Tech, CVL, IMM, FRGC, and AR databases show that our TENCSR outperforms some state-of-the-art algorithms. (C) 2018 SPIE and IS&T

关键词： image classification pattern recognition nonlocal constraint feature coding

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Time series-based hybrid ensemble learning model with multivariate multidimensional feature coding for DNA methylation prediction

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BMC GENOMICS 2023年第1期24卷 758-758页

作者： Wu, Yan Li, Tan Li, Mengshan Zhou, Weihong Sheng, Sheng Wang, Jun Wu, Fu-an Jiangsu Univ Sci & Technol Sch Biotechnol Zhenjiang 212018 Jiangsu Peoples R China Gannan Normal Univ Sch Math & Comp Sci Ganzhou 341000 Jiangxi Peoples R China Chinese Acad Agr Sci Sericultural Res Inst Zhenjiang 212018 Jiangsu Peoples R China Gannan Normal Univ Coll Phys & Elect Informat Ganzhou 341000 Jiangxi Peoples R China

BackgroundDNA methylation is a form of epigenetic modification that impacts gene expression without modifying the DNA sequence, thereby exerting control over gene function and cellular development. The prediction of DNA methylation is vital for understanding and exploring gene regulatory mechanisms. Currently, machine learning algorithms are primarily used for model construction. However, several challenges remain to be addressed, including limited prediction accuracy, constrained generalization capability, and insufficient learning *** response to the aforementioned challenges, this paper leverages the similarities between DNA sequences and time series to introduce a time series-based hybrid ensemble learning model, called Multi2-Con-CAPSO-LSTM. The model utilizes multivariate and multidimensional encoding approach, combining three types of time series encodings with three kinds of genetic feature encodings, resulting in a total of nine types of feature encoding matrices. Convolutional Neural Networks are utilized to extract features from DNA sequences, including temporal, positional, physicochemical, and genetic information, thereby creating a comprehensive feature matrix. The Long Short-Term Memory model is then optimized using the Chaotic Accelerated Particle Swarm Optimization algorithm for predicting DNA *** cross-validation experiments conducted on 17 species involving three types of DNA methylation (6 mA, 5hmC, and 4mC), the results demonstrate the robust predictive capabilities of the Multi2-Con-CAPSO-LSTM model in DNA methylation prediction across various types and species. Compared with other benchmark models, the Multi2-Con-CAPSO-LSTM model demonstrates significant advantages in sensitivity, specificity, accuracy, and correlation. The model proposed in this paper provides valuable insights and inspiration across various disciplines, including sequence alignment, genetic evolution, time series analysis, and struct

关键词： DNA methylation Time sequences feature coding Ensemble learning

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3D visual markup based on feature coding

Huazhong Ligong Daxue Xuebao/Journal Huazhong (Central China...

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Huazhong Ligong Daxue Xuebao/Journal Huazhong (Central China) University of Science and Technology 2001年第12期29卷 27-29页

作者： Wu, Lei Wu, Junjun College of Mech. Sci. and Eng. HUST Wuhan 430074 China

Based on feature coding mechanism, the association between the markup content and the solid model of design is researched and a method to markup 3D models visually is implemented. The members of a design group can view 3D models, make markups on the interesting aspects of the design and examine the markup content of other members. The approach can help to exchange the engineering information within the cooperative design group.

关键词： Cooperative design feature coding Markup Solid modeling

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IMAGE CLASSIFICATION BASED ON DEEP LOCAL feature coding

IMAGE CLASSIFICATION BASED ON DEEP LOCAL FEATURE CODING

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International Symposium on Intelligent Signal Processing and Communication Systems

作者： Qian Wang Jianqing Zhu Wei Shao Lei Wang Xiaobin Zhu Beijing Technology and Business University China College of Engineering Huaqiao University Quanzhou Fujian China CCTV High-Tech Television Devlelopment CO. Ltd China Academy of Broadcasting Science SAPPRFT China

In this paper, we propose an improved locally aggregated descriptor (VLAD) algorithm coded on deep local features for image classification. Firstly, convolutional neural network (CNN) is adopted to extract the dense local features of images. Secondly, a subset of feature, chosen by the criterion of normal distribution, is selected for high quality codebook generation. Finally, the local features are assigned to multi-neighbor visual words instead of the nearest one with different weights, simultaneously, the statistical distribution information about local features is taken into account during VLAD coding process. Extensive experiments on public available datasets demonstrate the promising performance of the proposed method against state-of-the-art methods.

关键词： VLAD CNN Image classification feature coding Multi-neighbor assignment

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Multiple Distance-Based coding: Toward Scalable feature Matching for Large-Scale Web Image Search

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IEEE TRANSACTIONS ON BIG DATA 2021年第3期7卷 559-573页

作者： Zhou, Zhili Wu, Q. M. Jonathan Sun, Xingming Nanjing Univ Informat Sci & Technol Jiangsu Engn Ctr Network Monitoring Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing 210044 Peoples R China Univ Windsor Dept Elect & Comp Engn Windsor ON N9B 3P4 Canada

For scalable feature matching in large-scale web image search, the bag-of-visual-words-based (BOW) approaches generally code local features as visual words to construct an inverted index file to match features efficiently. Both the popular feature coding techniques, i.e., K-means-based vector quantization and scalar quantization, directly quantize features to generate visual words. K-means-based vector quantization requires expensive visual codebook training, whereas scalar quantization leads to the miss of many matches due to the low stability of individual components of feature vectors. To address the above issues, we demonstrate that the corresponding sub-vectors of similar features generally have similar distances to multiple reference points in feature subspace and propose a multiple distance-based feature coding scheme for scalable feature matching. Specifically, based on the distances between the sub-vectors and multiple distinct reference points, we transform each feature to a set of feature codes, where one code is treated as a visual word required to construct the inverted index file whereas the others are embedded into the index file to further verify the feature matching based on the visual words. The proposed coding scheme does not need visual codebook training and shows desirable stability and discriminability. Moreover, in the matching verification, a feature-distance estimation method is proposed to estimate the Euclidean distances between features for an accurate matching verification. Extensive experimental results demonstrate the superiority of the proposed approach in comparison to the other approaches using recent feature quantization methods for large-scale web image search.

关键词： Visualization Encoding Indexes Training feature extraction Vector quantization Big data management web image search near-duplicate image search large-scale feature quantization feature coding

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WCE polyp detection based on novel feature descriptor with normalized variance locality-constrained linear coding

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INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY 2020年第8期15卷 1291-1302页

作者： Yang, Jianjun Chang, Liping Li, Sheng He, Xiongxiong Zhu, Tingwei Zhejiang Univ Technol Coll Informat Engn Hangzhou 310023 Zhejiang Peoples R China

Purpose Wireless capsule endoscopy (WCE) has become an effective facility to detect digestive tract diseases. To further improve the accuracy and efficiency of computer-aided diagnosis system in the detection of intestine polyps, a novel algorithm is proposed for WCE polyp detection in this paper. Methods First, by considering the rich color information of endoscopic images, a novel local color texture feature called histogram of local color difference (LCDH) is proposed for describing endoscopic images. A codebook acquisition method which is based upon positive samples is also proposed, generating more balanced visual words with the LCDH features. Furthermore, based on locality-constrained linear coding (LLC) algorithm, a normalized variance regular term is introduced as NVLLC algorithm, which considers the dispersion degree between k nearest visual words and features in the approximate coding phase. The final image representations are obtained from using the spatial matching pyramid model. Finally, the support vector machine is employed to classify the polyp images. Results The WCE dataset including 500 polyp and 500 normal images is adopted for evaluating the proposed method. Experimental results indicate that the classification accuracy, sensitivity and specificity have reached 96.00%, 95.80% and 96.20%, which performances better than traditional ways. Conclusion A novel method for WCE polyp detection is developed using LCDH feature descriptor and NVLLC coding scheme, which achieves a promising performance and can be implemented in clinical-assisted diagnosis of intestinal diseases.

关键词： CAD feature coding LCDH Normalized variance Polyp detection

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Color space quantization-based clustering for image retrieval

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Frontiers of Computer Science 2017年第6期11卷 1023-1035页

作者： Le DONG Wenpu DONG Ning FENG Mengdie MAO Long CHEN Gaipeng KONG School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu 611731 China

Color descriptors of an image are the most widely used visual features in content-based image retrieval sys- tems. In this study, we present a novel color-based image retrieval framework by integrating color space quantization and feature coding. Although color features have advantages such as robustness and simple extraction, direct processing of the abundant amount of color information in an RGB image is a challenging task. To overcome this problem, a color space clustering quantization algorithm is proposed to obtain the clustering color space （CCS） by clustering the CIE1976L＊a＊b＊ space into 256 distinct colors, which ade- quately accommodate human visual perception. In addition, a new feature coding method called feature-to-character coding （FCC） is proposed to encode the block-based main color fea- tures into character codes. In this method, images are repre- sented by character codes that contribute to efficiently build- ing an inverted index by using color features and by utilizing text-based search engines. Benefiting from its high-efficiency computation, the proposed framework can also be applied to large-scale web image retrieval. The experimental results demonstrate that the proposed system can produce a signifi- cant augmentation in performance when compared to block- based main color image retrieval systems that utilize the tra- ditional HSV（Hue, Saturation, Value） quantization method.

关键词： content-based image retrieval color spacequantization feature coding inverted index

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Action recognition via spatio-temporal local features: A comprehensive study

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IMAGE AND VISION COMPUTING 2016年 50卷 1-13页

作者： Zhen, Xiantong Shao, Ling Nanjing Univ Informat Sci & Technol Coll Elect & Informat Engn Nanjing Jiangsu Peoples R China Northumbria Univ Dept Comp Sci & Digital Technol Newcastle Upon Tyne NE1 8ST Tyne & Wear England Univ Sheffield Dept Elect & Elect Engn Sheffield S10 2TN S Yorkshire England

Local methods based on spatio-temporal interest points (STIPs) have shown their effectiveness for human action recognition. The bag-of-words (BoW) model has been widely used and dominated in this field. Recently, a large number of techniques based on local features including improved variants of the BoW model, sparse coding (SC), Fisher kernels (FK), vector of locally aggregated descriptors (VLAD) as well as the naive Bayes nearest neighbor (NBNN) classifier have been proposed and developed for visual recognition. However, some of them are proposed in the image domain and have not yet been applied to the video domain and it is still unclear how effectively these techniques would perform on action recognition. In this paper, we provide a comprehensive study on these local methods for human action recognition. We implement these techniques and conduct comparison under unified experimental settings on three widely used benchmarks, i.e., the KTH, UCF-YouTube and HMDB51 datasets. We discuss insightfully the findings from the experimental results and draw useful conclusions, which are expected to guide practical applications and future work for the action recognition community. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Action recognition Spatio-temporal local features feature coding Bag-of-words Sparse coding Fisher kernel VLAD NBNN Match kernels Performance evaluation

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Image classification based on saliency coding with category-specific codebooks

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NEUROCOMPUTING 2016年第0期184卷 188-195页

作者： Yang, Zhen Xiong, Huilin Shanghai Jiao Tong Univ Dept Automat 800 Dongchuan Rd Shanghai 200240 Peoples R China

This paper presents a feature encoding scheme for image classification by combining the salient coding method with the category-specific codebooks, which are generated separately using the training images of each category. Different from the usual way of concatenating or merging the category codebooks to form a global dictionary, we employ the category codebooks to calculate a type of category-sensitive saliency feature, and then, encode the saliency features to form a representation of image content. Compared to the state-of-the-art methods such as LC-KSVD, the dictionary generation and feature encoding in our scheme are pretty simple, and no complicated optimization is involved. However, our scheme can achieve better, in some cases, significantly better results, in terms of the classification accuracy, than the state-of-the-art methods. Extensive experiments are carried out to show the effectiveness of our method in comparing with various image classification methods. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Image classification feature coding Dictionary learning

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