Guest Editorial: Large-Scale Multimedia Data Retrieval, Classification, and Understanding

2017 ◽  
Vol 19 (9) ◽  
pp. 1965-1967 ◽  
Author(s):  
J. Song ◽  
H. Jegou ◽  
C. Snoek ◽  
Q. Tian ◽  
N. Sebe
Keyword(s):  
Large Scale ◽  
Guest Editorial ◽  
Data Retrieval ◽  
Multimedia Data

scholarly journals Deep Unsupervised Hashing for Large-Scale Cross-Modal Retrieval Using Knowledge Distillation Model

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Mingyong Li ◽  
Qiqi Li ◽  
Lirong Tang ◽  
Shuang Peng ◽  
Yan Ma ◽  
...  
Keyword(s):  
Large Scale ◽  
Data Retrieval ◽  
Multimedia Data ◽  
Search Performance ◽  
Similarity Matrix ◽  
Student Model ◽  
Deep Hashing ◽  
Knowledge Distillation ◽  
Semantic Alignment ◽  
Teacher Model

Cross-modal hashing encodes heterogeneous multimedia data into compact binary code to achieve fast and flexible retrieval across different modalities. Due to its low storage cost and high retrieval efficiency, it has received widespread attention. Supervised deep hashing significantly improves search performance and usually yields more accurate results, but requires a lot of manual annotation of the data. In contrast, unsupervised deep hashing is difficult to achieve satisfactory performance due to the lack of reliable supervisory information. To solve this problem, inspired by knowledge distillation, we propose a novel unsupervised knowledge distillation cross-modal hashing method based on semantic alignment (SAKDH), which can reconstruct the similarity matrix using the hidden correlation information of the pretrained unsupervised teacher model, and the reconstructed similarity matrix can be used to guide the supervised student model. Specifically, firstly, the teacher model adopted an unsupervised semantic alignment hashing method, which can construct a modal fusion similarity matrix. Secondly, under the supervision of teacher model distillation information, the student model can generate more discriminative hash codes. Experimental results on two extensive benchmark datasets (MIRFLICKR-25K and NUS-WIDE) show that compared to several representative unsupervised cross-modal hashing methods, the mean average precision (MAP) of our proposed method has achieved a significant improvement. It fully reflects its effectiveness in large-scale cross-modal data retrieval.

scholarly journals Semantic-guided autoencoder adversarial hashing for large-scale cross-modal retrieval

2022 ◽  
Author(s):  
Mingyong Li ◽  
Qiqi Li ◽  
Yan Ma ◽  
Degang Yang
Keyword(s):  
Large Scale ◽  
Research Work ◽  
Feature Learning ◽  
Data Retrieval ◽  
Mobile Internet ◽  
Multimedia Data ◽  
Internet Technology ◽  
Learning Ability ◽  
Smart Devices ◽  
Network Training

AbstractWith the vigorous development of mobile Internet technology and the popularization of smart devices, while the amount of multimedia data has exploded, its forms have become more and more diversified. People’s demand for information is no longer satisfied with single-modal data retrieval, and cross-modal retrieval has become a research hotspot in recent years. Due to the strong feature learning ability of deep learning, cross-modal deep hashing has been extensively studied. However, the similarity of different modalities is difficult to measure directly because of the different distribution and representation of cross-modal. Therefore, it is urgent to eliminate the modal gap and improve retrieval accuracy. Some previous research work has introduced GANs in cross-modal hashing to reduce semantic differences between different modalities. However, most of the existing GAN-based cross-modal hashing methods have some issues such as network training is unstable and gradient disappears, which affect the elimination of modal differences. To solve this issue, this paper proposed a novel Semantic-guided Autoencoder Adversarial Hashing method for cross-modal retrieval (SAAH). First of all, two kinds of adversarial autoencoder networks, under the guidance of semantic multi-labels, maximize the semantic relevance of instances and maintain the immutability of cross-modal. Secondly, under the supervision of semantics, the adversarial module guides the feature learning process and maintains the modality relations. In addition, to maintain the inter-modal correlation of all similar pairs, this paper use two types of loss functions to maintain the similarity. To verify the effectiveness of our proposed method, sufficient experiments were conducted on three widely used cross-modal datasets (MIRFLICKR, NUS-WIDE and MS COCO), and compared with several representatives advanced cross-modal retrieval methods, SAAH achieved leading retrieval performance.

scholarly journals Guest Editorial: Special Issue on Large-Scale Wireless-Powered Networks With Backscatter Communications

2020 ◽  
Vol 1 ◽  
pp. 1961-1964
Author(s):  
Sami Muhaidat ◽  
Paschalis C. Sofotasios ◽  
Kaibin Huang ◽  
Muhammad Ali Imran ◽  
Zhiguo Ding ◽  
...  
Keyword(s):  
Large Scale ◽  
Guest Editorial ◽  
Special Issue ◽  
Editorial Special Issue

scholarly journals Temporal Activity Path Based Character Correction in Heterogeneous Social Networks via Multimedia Sources

2018 ◽  
Vol 2018 ◽  
pp. 1-16
Author(s):  
Jun Long ◽  
Lei Zhu ◽  
Zhan Yang ◽  
Chengyuan Zhang ◽  
Xinpan Yuan
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Large Scale ◽  
Multimedia Data ◽  
Attribute Information ◽  
Novel Method ◽  
Relationship Network ◽  
Heterogeneous Social Networks ◽  
The Relationship ◽  
Over Time

Vast amount of multimedia data contains massive and multifarious social information which is used to construct large-scale social networks. In a complex social network, a character should be ideally denoted by one and only one vertex. However, it is pervasive that a character is denoted by two or more vertices with different names; thus it is usually considered as multiple, different characters. This problem causes incorrectness of results in network analysis and mining. The factual challenge is that character uniqueness is hard to correctly confirm due to lots of complicated factors, for example, name changing and anonymization, leading to character duplication. Early, limited research has shown that previous methods depended overly upon supplementary attribute information from databases. In this paper, we propose a novel method to merge the character vertices which refer to the same entity but are denoted with different names. With this method, we firstly build the relationship network among characters based on records of social activities participating, which are extracted from multimedia sources. Then we define temporal activity paths (TAPs) for each character over time. After that, we measure similarity of the TAPs for any two characters. If the similarity is high enough, the two vertices should be considered as the same character. Based on TAPs, we can determine whether to merge the two character vertices. Our experiments showed that this solution can accurately confirm character uniqueness in large-scale social network.

scholarly journals Community-curated and standardised metadata of published ancient metagenomic samples with AncientMetagenomeDir

2020 ◽  
Author(s):  
James A. Fellows Yates ◽  
Aida Andrades Valtueña ◽  
Ashild J. Vågene ◽  
Becky Cribdon ◽  
Irina M. Velsko ◽  
...  
Keyword(s):  
Large Scale ◽  
Genetic Data ◽  
Data Retrieval ◽  
Data Sources ◽  
Valuable Data ◽  
Dna And Rna ◽  
Wide Range ◽  
Evolutionary Studies ◽  
Meta Analyses ◽  
Microbial Samples

ABSTRACTAncient DNA and RNA are valuable data sources for a wide range of disciplines. Within the field of ancient metagenomics, the number of published genetic datasets has risen dramatically in recent years, and tracking this data for reuse is particularly important for large-scale ecological and evolutionary studies of individual microbial taxa, microbial communities, and metagenomic assemblages. AncientMetagenomeDir (archived at https://doi.org/10.5281/zenodo.3980833) is a collection of indices of published genetic data deriving from ancient microbial samples that provides basic, standardised metadata and accession numbers to allow rapid data retrieval from online repositories. These collections are community-curated and span multiple sub-disciplines in order to ensure adequate breadth and consensus in metadata definitions, as well as longevity of the database. Internal guidelines and automated checks to facilitate compatibility with established sequence-read archives and term-ontologies ensure consistency and interoperability for future meta-analyses. This collection will also assist in standardising metadata reporting for future ancient metagenomic studies.

Data Storage, Retrieval and Management

2010 ◽  
pp. 111-140
Author(s):  
Valentin Cristea ◽  
Ciprian Dobre ◽  
Corina Stratan ◽  
Florin Pop
Keyword(s):  
Data Storage ◽  
Resource Sharing ◽  
High Performance ◽  
Large Scale ◽  
Workflow Management ◽  
Large Data ◽  
Data Retrieval ◽  
Distributed Data Storage ◽  
Processing Power ◽  
Data Transfers

The latest advances in network and distributedsystem technologies now allow integration of a vast variety of services with almost unlimited processing power, using large amounts of data. Sharing of resources is often viewed as the key goal for distributed systems, and in this context the sharing of stored data appears as the most important aspect of distributed resource sharing. Scientific applications are the first to take advantage of such environments as the requirements of current and future high performance computing experiments are pressing, in terms of even higher volumes of issued data to be stored and managed. While these new environments reveal huge opportunities for large-scale distributed data storage and management, they also raise important technical challenges, which need to be addressed. The ability to support persistent storage of data on behalf of users, the consistent distribution of up-to-date data, the reliable replication of fast changing datasets or the efficient management of large data transfers are just some of these new challenges. In this chapter we discuss how the existing distributed computing infrastructure is adequate for supporting the required data storage and management functionalities. We highlight the issues raised from storing data over large distributed environments and discuss the recent research efforts dealing with challenges of data retrieval, replication and fast data transfers. Interaction of data management with other data sensitive, emerging technologies as the workflow management is also addressed.

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