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Social Media Retrieval Using Image Features and Structured Text
[chapter]
Lecture Notes in Computer Science
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(text and image) data. ...
XML information retrieval is different from standard text retrieval in two aspects: the XML structure may be of interest as part of the query; and the information does not have to be text. ...
This research was undertaken using facilities supported by the Australian Research Council, an RMIT VRII grant, and a scholarship provided by the Malaysian Ministry of Higher Education. ...
doi:10.1007/978-3-540-73888-6_35
fatcat:yeyez5ivqzghneqtwl67hcxrf4
Deep Learning Techniques for Future Intelligent Cross-Media Retrieval
[article]
2020
arXiv
pre-print
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Then, we present some well-known cross-media datasets used for retrieval, considering the importance of these datasets in the context in of deep learning based cross-media retrieval approaches. ...
and the potential solutions of deep learning assisted cross-media retrieval. ...
Text Image and Audio Image and Text Supervised Semantic Hashing (DVSH) model for cross-media retrieval. ...
arXiv:2008.01191v1
fatcat:t63bg55w2vdqjcprzaaidrmprq
Cross-Media Semantic Matching based on Sparse Representation
2019
Tehnički Vjesnik
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In this paper, the cross-media retrieval includes two tasks: query image retrieves relevant text and query text retrieves relevant images. ...
With the development of sparse representation, two independent sparse representation classifiers are used to map the heterogeneous features of images and texts into their common semantic space before implementing ...
People begin to use the image to retrieve the similar images or texts, or use the keywords and textual document to retrieve the related images and videos. ...
doi:10.17559/tv-20190730110003
fatcat:yi4if55xa5b4xksjqfvzu7hoku
Self-Supervised Learning from Web Data for Multimodal Retrieval
[article]
2019
arXiv
pre-print
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We show that the embeddings learnt with Web and Social Media data have competitive performances over supervised methods in the text based image retrieval task, and we clearly outperform state of the art ...
Web and Social Media platforms provide a virtually unlimited amount of this multimodal data. ...
Industrials program from the Generalitat de Catalunya, the Spanish project TIN2017-89779-P, the H2020 Marie Skłodowska-Curie actions of the European Union, grant agreement No 712949 (TECNIOspring PLUS), and ...
arXiv:1901.02004v1
fatcat:wpibqwyf2rax7ltrahjnw6vvxy
Multimedia Indexing, Search, and Retrieval in Large Databases of Social Networks
[chapter]
2012
Computer Communications and Networks
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In the first part pure multimedia content retrieval issues are presented, while in the second part, the social aspects and new, interesting views on multimedia retrieval in the large social media databases ...
In the social media era, multimedia content search is promoted to a fundamental feature towards efficient search inside social multimedia streams, content classification, context and event based indexing ...
Their experiments were evaluated in a tag-based social image retrieval framework where the well known Okapi BM25 ranking function for text retrieval was used [52] . ...
doi:10.1007/978-1-4471-4555-4_3
dblp:series/ccn/SemertzidisRTSD13
fatcat:6l3whv5qcjgshmjwcqh6dgelb4
Learning to Learn from Web Data Through Deep Semantic Embeddings
[chapter]
2019
Lecture Notes in Computer Science
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We show that the embeddings learnt with Web and Social Media data have competitive performances over supervised methods in the text based image retrieval task, and we clearly outperform state of the art ...
In this paper we propose to learn a multimodal image and text embedding from Web and Social Media data, aiming to leverage the semantic knowledge learnt in the text domain and transfer it to a visual model ...
Industrials program from the Generalitat de Catalunya, the Spanish project TIN2017-89779-P, the H2020 Marie Skłodowska-Curie actions of the European Union, grant agreement No 712949 (TECNIOspring PLUS), and ...
doi:10.1007/978-3-030-11024-6_40
fatcat:crzepesrz5bglj3bmunkldk6ey
Learning to Learn from Web Data through Deep Semantic Embeddings
[article]
2018
arXiv
pre-print
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We show that the embeddings learnt with Web and Social Media data have competitive performances over supervised methods in the text based image retrieval task, and we clearly outperform state of the art ...
In this paper we propose to learn a multimodal image and text embedding from Web and Social Media data, aiming to leverage the semantic knowledge learnt in the text domain and transfer it to a visual model ...
Industrials program from the Generalitat de Catalunya, the Spanish project TIN2017-89779-P, the H2020 Marie Skłodowska-Curie actions of the European Union, grant agreement No 712949 (TECNIOspring PLUS), and ...
arXiv:1808.06368v1
fatcat:m4dtjcxevnfmdgz7z47rzulv3e
Neural Network-Based Dynamic Segmentation and Weighted Integrated Matching of Cross-Media Piano Performance Audio Recognition and Retrieval Algorithm
2022
Computational Intelligence and Neuroscience
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This paper presents a dynamic segmentation and weighted comprehensive matching algorithm based on neural networks for cross-media piano performance audio recognition and retrieval. ...
This paper implements the data collection and processing, audio recognition, and retrieval algorithm for cross-media piano performance big data through three main modules: the collection, processing, and ...
Wu and Ding applied natural language processing technology to text retrieval to extract text semantic features, fully exploit document topic features, and significantly improve the matching with topic ...
doi:10.1155/2022/9323646
pmid:35602641
pmcid:PMC9122679
fatcat:lmgefjsmdzagdekurjwqs6xwju
Multiple feature fusion for social media applications
2010
Proceedings of the 2010 international conference on Management of data - SIGMOD '10
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Using that, we design an efficient retrieval algorithm for large social media data. Further, we integrate temporal information into the probabilistic model for social media recommendation. ...
Recent literature has noted the generality of multiple features in the social media environment, such as textual, visual and user information. ...
A PROBABILISTIC MODEL FOR SOCIAL MEDIA RETRIEVAL In this section, we introduce our approach for multimedia retrieval in social media environment, which can effectively integrate the multiple features and ...
doi:10.1145/1807167.1807216
dblp:conf/sigmod/CuiTZZ10
fatcat:szaoxvmcwjekzhnku4ta3avhcu
Convolutional Neural Network-Based Cross-Media Semantic Matching and User Adaptive Satisfaction Analysis Model
2022
Computational Intelligence and Neuroscience
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the feature information of the image and uses dilated instead of traditional convolution. ...
The spatial correlation of cross-media semantic matching further improves the classification accuracy of hyperspectral images and reduces the classification time under user adaptive satisfaction complexity ...
After extracting text and image features using deep learning methods, the method then uses deep semantic matching methods to complete the cross-media retrieval part, simply a three-layer fully connected ...
doi:10.1155/2022/4244675
pmid:35535181
pmcid:PMC9078763
fatcat:5gdiv7u6rra6xjcmfe4jim77ne
Web-Scale Multimedia Information Networks
2012
Proceedings of the IEEE
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be used to do inference effectively). ...
Ontology and cross-media structures are constructed and expanded by automatically constructing MINets from web-scale data by state-of-the-art information extraction and knowledge-based population techniques ...
as social media and webpages. ...
doi:10.1109/jproc.2012.2201909
fatcat:4hcia4agvbaija2kx5ynj2esse
Early_and_Late_Fusion_of_Multiple_Modalities_in_Sentinel_Image_Retrieval
2020
Zenodo
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and social media data sources. ...
On the other hand, the late fusion mechanism exploits the context of other geo-referenced data such as social media retrieval, to further enrich the list of retrieved Sentinel image patches. ...
Acknowledgements This work has been supported by the EC-funded projects CANDELA (H2020-776193) and EOPEN (H2020-776019). ...
doi:10.5281/zenodo.4280738
fatcat:qv7nqqqumrbifodqt5jrgvkhva
Learning Shared Semantic Space with Correlation Alignment for Cross-modal Event Retrieval
[article]
2019
arXiv
pre-print
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2019 pre-print arXiv:1901.04268v2 fatcat:bjekuqbienbq7axxuhacmfvhouOther Versions
In the context of cross-modal (event) retrieval, we design a neural network with convolutional layers and fully-connected layers to extract features for images, including images on Flickr-like social media ...
Simultaneously, we exploit a fully-connected neural network to extract semantic features for texts, including news articles from news media. ...
neural networks trained on images and texts. We contribute a weakly-aligned unpaired Wiki-Flickr Event dataset as a complement of the existing paired datasets for cross-modal retrieval. ...
arXiv:1901.04268v3
fatcat:hipjb7ba2fg3hp5g5d3oq3kaki
Automated Detection of Substance Use-Related Social Media Posts Based on Image and Text Analysis
2017
2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI)
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To detect substance use related social media posts, we employ the state-of-the-art social media analytics that combines Neural Network-based image and text processing technologies. ...
Our evaluation results demonstrate that image features derived using Convolutional Neural Network and textual features derived using neural document embedding are effective in identifying substance use-related ...
Acknowledgment The authors would like to thank Arash Fallah, Yahya Almazni and Nirajkumar Lohbare for their contributions to the data annotation process. ...
doi:10.1109/ictai.2017.00122
dblp:conf/ictai/RoyPPP17
fatcat:f6eqi3fljrd45krmg2mqqqibbq
Text Analytics in Social Media
[chapter]
2012
Mining Text Data
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We next discuss the research progress of applying text analytics in social media from different perspectives, and show how to improve existing approaches to text representation in social media, using real-world ...
In this chapter, we first introduce the background of traditional text analytics and the distinct aspects of textual data in social media. ...
Acknowledgments This work is, in part, supported by the grants NSF (#0812551), ONR (N000141010091) and AFOSR (FA95500810132). ...
doi:10.1007/978-1-4614-3223-4_12
fatcat:ynmfabrhpjf6vils663o3rs2za
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