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Unsupervised Cross-Media Hashing Learning via Knowledge Graph

Chinese Journal of Electronics 2022年第6期31卷 1081-1091页

作者： YE Zhaoda HE Xiangteng PENG Yuxin Wangxuan Institute of Computer Technology Peking University

With the rapid growth of multimedia data, cross-media hashing has become an important technology for fast cross-media retrieval. Because the manual annotations are difficult to obtain in real-world application, unsupervised cross-media hashing is studied to address the hashing learning without manual *** unsupervised cross-media hashing methods generally focus on calculating the similarities through the features of multimedia data, while the learned hashing code cannot reflect the semantic relationship among the multimedia data, which hinders the accuracy in the cross-media retrieval. When humans try to understand multimedia data, the knowledge of concept relations in our brain plays an important role in obtaining high-level *** by this, we propose a knowledge guided unsupervised cross-media hashing(KGUCH) approach, which applies the knowledge graph to construct high-level semantic correlations for unsupervised cross-media hash *** contributions in this paper can be summarized as follows: 1) The knowledge graph is introduced as auxiliary knowledge to construct the semantic graph for the concepts in each image and text instance, which can bridge the multimedia data with high-level semantic correlations to improve the accuracy of learned hash codes for crossmedia retrieval. 2) The proposed KGUCH approach constructs correlation of the multimedia data from both the semantic and the feature aspects, which can exploit complementary information to promote the unsupervised cross-media hash learning. The experiments are conducted on three widely-used datasets, which verify the effectiveness of our proposed KGUCH approach.

关键词： knowledge graph fast cross-media retrieval multimedia data semantic graph unsupervised cross-media hashing methods learned hash codes feature extraction file organisation graph theory semantic networks unsupervised cross-media hashing learning unsupervised cross-media hashing approach multimedia computing information retrieval manual annotations learning (artificial intelligence) learned hashing code high-level semantic correlations unsupervised learning

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Salt and pepper noise removal in surveillance video based on low-rank matrix recovery

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Computational Visual Media 2015年第1期1卷 59-68页

作者： Yongxia Zhang Yi Liu Xuemei Li Caiming Zhang School of Computer Science and Technology Shandong University Shandong University of Finance and Economics Shandong Provincial Key Laboratory of Digital Media Technology

This paper proposes a new algorithm based on low-rank matrix recovery to remove salt &pepper noise from surveillance video. Unlike single image denoising techniques, noise removal from video sequences aims to utilize both temporal and spatial information. By grouping neighboring frames based on similarities of the whole images in the temporal domain, we formulate the problem of removing salt &pepper noise from a video tracking sequence as a lowrank matrix recovery problem. The resulting nuclear norm and L1-norm related minimization problems can be efficiently solved by many recently developed methods. To determine the low-rank matrix, we use an averaging method based on other similar images. Our method can not only remove noise but also preserve edges and details. The performance of our proposed approach compares favorably to that of existing algorithms and gives better PSNR and SSIM results.

关键词： multimedia computing noise cancellation signal denoising sparse matrices video signal processing video surveillance

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AN OBJECT ORIENTED MODEL SCHEDULING FOR MEDIA-SOC

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Journal of Electronics(China) 2009年第2期26卷 244-251页

作者： Cheng Xingmei Yao Yingbiao Zhang Yixiong Liu Peng Yao Qingdong Department of Information Science and Electronic Engineering Zhejiang University Hangzhou 310027 China College of Communication Engineering Hangzhou Dianzi University Hangzhou 310018 China

This paper proposes an object oriented model scheduling for parallel computing in media MultiProcessors System on Chip(MPSoC).Firstly,the Coarse Grain Data Flow Graph(CGDFG) parallel programming model is used in this ***,this approach has the feature of unified abstraction for software objects implementing in processor and hardware objects implementing in ASICs,easy for mapping CGDFG programming on *** approach cuts down the kernel overhead and reduces the code size *** principle of the oriented object model,the method of scheduling,and how to map a parallel programming through CGDFG to the MPSoC are analyzed in this *** approach also compares the code size and execution cycles with conventional control flow scheduling,and presents respective management overhead for one application in me-dia-SoC.

关键词： multimedia computing Coarse Grain Data Flow Graph(CGDFG) Parallel program-ming Real Time Operating System(RTOS) MultiProcessors System on Chip(MPSoC)

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