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The 1st workshop on large-scale multimedia retrieval and mining (LS-MMRM'09)

Rong Yan, John R. Smith, Qi Tian, Rahul Sukthankar
2009 Proceedings of the seventeen ACM international conference on Multimedia - MM '09  
This workshop, as the first of its kind, aims to bring together researchers and industrial practitioners interested in large-scale multimedia data retrieval and mining.  ...  The workshop will provide a venue for the participants to explore a variety of aspects and applications on how advanced multimedia analysis techniques can be leveraged to address the challenges in large-scale  ...  ., on the other hand, propose a robust subspace bagging (RB-SBag) learning algorithm, as well as its MapReduce implementation on Hadoop with a new scheduling algorithm.  ... 
doi:10.1145/1631272.1631545 dblp:conf/mm/YanSTS09 fatcat:a6qyl7hfbbccnggcud7sx6do4q

Cross-media cloud computing

Zhongzhi Shi, Guang Jiang, Bo Zhang, Jinpeng Yue, Xiaofei Zhao
2012 2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems  
Cross-media is the outstanding characteristics of the age of big data with large scale and complicated processing task.  ...  Furthermore, we propose a framework for cross-media semantic understanding which contains discriminative modeling, generative modeling and cognitive modeling.  ...  In semantic memory, ontology is used to specify a conceptualization of a domain using concepts, attributes, and relations.  ... 
doi:10.1109/ccis.2012.6664429 dblp:conf/ccis/ShiJZYZ12 fatcat:wefyscguf5bmjj2uzjafdjc2ty

Visual Semantic Based 3D Video Retrieval System Using HDFS

2016 KSII Transactions on Internet and Information Systems  
In this ambit, we delve into 3D-CBVR (Content Based Video Retrieval) concept for the first time. For this purpose we intent to hitch on BOVW and Mapreduce in 3D framework.  ...  This paper brings out a neoteric frame of reference for visual semantic based 3d video search and retrieval applications.  ...  In [35] , authors discussed about semantic based data representation, which uses the technique of robust structured subspace learning to guarantee the subspace using L2,1-norm.  ... 
doi:10.3837/tiis.2016.08.021 fatcat:kmu7zx5vdvh4pk6g3pe3voubcm

High-level event recognition in unconstrained videos

Yu-Gang Jiang, Subhabrata Bhattacharya, Shih-Fu Chang, Mubarak Shah
2012 International Journal of Multimedia Information Retrieval  
Such videos depicting complex events have limited quality control, and therefore, may include severe camera motion, poor lighting, heavy background clutter, and occlusion.  ...  While the existing solutions vary, we identify common key modules and provide detailed descriptions along with some insights for each of them, including extraction and representation of low-level features  ...  Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied  ... 
doi:10.1007/s13735-012-0024-2 fatcat:mfzttic3svb4tho2xb6aczgp4y

Intelligent video processing using data mining techniques

Kimiaki Shirahama
2011 ACM SIGMultimedia Records  
Finally, to improve retrieve performance, the integration of the QBK and QBE interfaces is explored where informations from text and image/video modalities are iii  ...  So, patterns for defining meaningful similarities are extracted using example videos.  ...  Related Works The most popular video ontology is Large-Scale Concept Ontology for Multimedia (LSCOM) [71] . This targets broadcast news videos and defines a standardized set of 1, 000 concepts.  ... 
doi:10.1145/2069203.2069207 fatcat:66pv4qvzofgz3oyanwu6dgjcoa

New trends and ideas in visual concept detection

Mark J. Huiskes, Bart Thomee, Michael S. Lew
2010 Proceedings of the international conference on Multimedia information retrieval - MIR '10  
Additionally, we present retrieval results obtained by relevance feedback methods, demonstrating (i) how their performance can be enhanced using features based on visual concept classifiers, and (ii) how  ...  Additionally, we identify a number of promising trends and ideas in visual concept detection.  ...  This includes a discussion of the 2009 ImageCLEF large-scale visual concept detection task, which used the MIR Flickr image collection.  ... 
doi:10.1145/1743384.1743475 dblp:conf/mir/HuiskesTL10 fatcat:cvnawyrgzrd3zodaxbkxeu2ae4

Front Matter: Volume 8745

Ivan Kadar
2013 Signal Processing, Sensor Fusion, and Target Recognition XXII  
(United States) SESSION 5 MULTISENSOR FUSION METHODOLOGIES AND APPLICATIONS III 8745 0M A robust technique for semantic annotation of group activities based on recognition of extracted features in video  ... Paper Numbering: Proceedings of SPIE follow an e-First publication model, with papers published first online and then in print and on CD-ROM.  ...  FA8750-12-C-0315 Advanced Machine Learning & Statistical Inference Approaches for Big Data Analytics and Information Fusion Proprietary & Confidential  ... 
doi:10.1117/12.2031900 fatcat:dwkpv5gaqfemlpqebcgmquu63i

A Comprehensive Review of Visual-Textual Sentiment Analysis from Social Media Networks [article]

Israa Khalaf Salman Al-Tameemi, Mohammad-Reza Feizi-Derakhshi, Saeed Pashazadeh, Mohammad Asadpour
2022 arXiv   pre-print
benchmark datasets, and the efficacy of multiple classification methodologies suited to each field.  ...  Finally, we highlight the most significant challenges and investigate several important sentiment applications.  ...  One of its advantages is that it incorporates more classes in well-known visual ontologies, such as large-scale concept ontology for multimedia and ImageNet.  ... 
arXiv:2207.02160v1 fatcat:l3vxpjnqkrfthkvhdldwonpoe4

Applied machine learning and management of volatility, uncertainty, complexity & ambiguity (V.U.C.A)

Srikanta Patnaik
2020 Journal of Intelligent & Fuzzy Systems  
The authors claimed that the model computed a covariance matrix from the multi-body rotation characters and constructs a feature subspace using a covariance matrix.  ...  The authors have used fuzzy system theory and the word vector semantic clus-tering for features selection from social media data.  ...  model by combining the naive Bayesian classification algorithm with the fuzzy model and validated their model in teaching practice.  ... 
doi:10.3233/jifs-179915 fatcat:njwjogoperg2jggdkyheiibbhi

Large-scale retrieval for medical image analytics: A comprehensive review

Zhongyu Li, Xiaofan Zhang, Henning Müller, Shaoting Zhang
2018 Medical Image Analysis  
On the basis of existing work, we introduce the evaluation protocols and multiple applications of large-scale medical image retrieval, with a variety of exploratory and diagnostic scenarios.  ...  Specifically, we first present the general pipeline of large-scale retrieval, summarize the challenges/opportunities of medical image analytics on a large-scale.  ...  However, if we 1212 re-train a large-scale model every time from scratch, using both the original and the newly added images, it is time-consuming and adversely affects the 1214 efficiency of medical retrieval  ... 
doi:10.1016/ pmid:29031831 fatcat:s6jnxawnongufgdngpjeifv3vm

Open challenges for data stream mining research

Georg Krempl, Myra Spiliopoulou, Jerzy Stefanowski, Indre Žliobaite, Dariusz Brzeziński, Eyke Hüllermeier, Mark Last, Vincent Lemaire, Tino Noack, Ammar Shaker, Sonja Sievi
2014 SIGKDD Explorations  
While predictive modeling for data streams and big data have received a lot of attention over the last decade, many research approaches are typically designed for well-behaved controlled problem settings  ...  Each study exploits its own tools and mechanisms to capture, store, query and analyze Twitter data.  ...  Acknowledgments We would like to thank the participants of the RealStream2013 workshop at ECMLPKDD2013 in Prague, and in particular Bernhard Pfahringer and George Forman, for suggestions and discussions  ... 
doi:10.1145/2674026.2674028 fatcat:y3bozzeohveibgxb5wmiwfcogm

Reconstructing Hardware Transactional Memory for Workload Optimized Systems [chapter]

Kunal Korgaonkar, Prabhat Jain, Deepak Tomar, Kashyap Garimella, Veezhinathan Kamakoti
2011 Lecture Notes in Computer Science  
With the continuity of Moore's law in the multicore era and the emerging cloud computing, parallelism has been pervasively available almost everywhere, from traditional processor pipelines to large-scale  ...  The two-day technical program of APPT 2011 provided an excellent venue capturing the state of the art and practice in parallel architectures, parallel software and distributed and cloud computing.  ...  MapReduce [1] , proposed by Google, is a model to program large-scale parallel and distributed systems.  ... 
doi:10.1007/978-3-642-24151-2_1 fatcat:32cx745cn5cfdm5sbeah6eyiey

Image Steganography Using HBC and RDH Technique

Hemalatha M, Prasanna A, Dinesh Kumar R, Vinothkumar D
2014 International Journal of Computer Applications Technology and Research  
Reverse Data Hiding (RDH) is used to get the original image and it proceeds once when all the corners are unlocked with proper secret keys.  ...  Here Integer Cosine Transform (ICT) and Integer Wavelet Transform (IWT) is combined for converting signal to frequency.  ...  Cloud computing is a specialised distributed computing on a large-scale [7] .  ... 
doi:10.7753/ijcatr0303.1001 fatcat:4i6tujs4oje2tnxf5c25eh26x4

Detecting Events and Patterns in Large-Scale User Generated Textual Streams with Statistical Learning Methods [article]

Vasileios Lampos
2012 arXiv   pre-print
An important intermediate task regards the formation and identification of features which characterise a target event; we select and use those textual features in several linear, non-linear and hybrid  ...  Twitter, using Statistical Machine Learning methods.  ...  First Steps on Event Detection in Large-Scale Textual Streams 72 choice; how can one know that the optimal set of markers is being used, especially when she deals with a vague concept?  ... 
arXiv:1208.2873v1 fatcat:j75gy2o7qzbo7aqgms6t3nqnji

Distributed Local Outlier Factor with Locality-Sensitive Hashing

Lining Zheng, University, My, University, My
With cross-partition updating, the actual global k-nearest neighbors (k-NN) of the outlier candidates are found, and the related information of the neighbors is used to update the outlier scores of the  ...  The results also highlight that MR-LOF-LSH scales well as the cluster size increases.  ...  We make use of several concepts and functions from MapReduce and Apache Spark to better explain various stages in this approach.  ... 
doi:10.20381/ruor-24060 fatcat:qfig7oixindjnev4kpo7yls4z4
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