相关论文
2008 - PAKDD
Applying Latent Semantic Indexing in Frequent Itemset Mining for Document Relation Discovery

Word-based relations among technical documents are immensely useful information but often hidden in a large amount of scientific publications. This work presents a method to apply latent semantic indexing in frequent itemset mining to discover potential relations among scientific publications. In this work, two weighting schemes, tf and tfidf are investigated with the exploitation of latent semantic indexing. The proposed method is evaluated using a set of technical documents in a publication database by comparing the extracted document relations with their references (citations). To this end, the paper uses order accumulative citation matrices to evaluate the validity (quality) of discovered patterns. The results also show that the proposed method successfully discovers a set of document relations, comparing to the original method that uses no latent semantic indexing.

2018 - ArXiv
Understanding the Gist of Images - Ranking of Concepts for Multimedia Indexing

Nowadays, where multimedia data is continuously generated, stored, and distributed, multimedia indexing, with its purpose of group- ing similar data, becomes more important than ever. Understanding the gist (=message) of multimedia instances is framed in related work as a ranking of concepts from a knowledge base, i.e., Wikipedia. We cast the task of multimedia indexing as a gist understanding problem. Our pipeline benefits from external knowledge and two subsequent learning- to-rank (l2r) settings. The first l2r produces a ranking of concepts rep- resenting the respective multimedia instance. The second l2r produces a mapping between the concept representation of an instance and the targeted class topic(s) for the multimedia indexing task. The evaluation on an established big size corpus (MIRFlickr25k, with 25,000 images), shows that multimedia indexing benefits from understanding the gist. Finally, with a MAP of 61.42, it can be shown that the multimedia in- dexing task benefits from understanding the gist. Thus, the presented end-to-end setting outperforms DBM and competes with Hashing-based methods.

1997 - Int. J. Comput. Linguistics Chin. Lang. Process.
An Assessment on Character-based Chinese News Filtering Using Latent Semantic Indexing

We assess the Latent Semantic Indexing (LSI) approach to Chinese information filtering. In particular, the approach is for Chinese news filtering agents that use a character-based and hierarchical filtering scheme. The traditional vector space model is employed as an information filtering model, and each document is converted into a vector of weights of terms. Instead of using words as terms in the JR nominating tradition, terms refer to Chinese characters. LSI captures the semantic relationship between documents and Chinese characters. We use the Sin-gular-value Decomposition (SVD) technique to compress the term space into a lower dimension which achieves latent association between documents and terms. The results of experiments show that the recall and precision rates of Chinese news filtering using the character-based ap-proach incorporating the LSI technique are satisfactory.

1999 - Interactive Learning Environments
Modeling and Diagnosing Domain Knowledge Using Latent Semantic Indexing

A Latent Semantic Index (LSI) was constructed from arguments made by navy officers concerning events in an anti-air warfare scenario. A model based on LSI factor values predicted level of domain expertise with 89% accuracy. The LSI factor space was reduced using MDS to five dimensions: aircraft route, aircraft response, kinematics, localization, and an unclassifiable element. Arguments in the localization category were reliably more common among officers with the greatest expertise. Automated classification of arguments into these elements achieved 84% accuracy. LSI may be a useful tool for automating aspects of modeling expertise and diagnosing knowledge deficiencies.

2014 - TRECVID
AXES @ TRECVID 2014: Instance Search

The AXES project submitted three runs for instance search at TRECVid 2014 [1]: two interactive runs and one automatic run. These runs used exactly the same instance search engine as was used by AXES in 2013, but incorporate a custom interface for the instance search tasks, pseudo relevance feedback, and result list expansion. Unfortunately, these changes did not improve accuracy significantly over our 2013 results in the interactive task.

2015 - CLEF
USI at BioASQ 2015: a Semantic Similarity-based Approach for Semantic Indexing

The need of indexing biomedical papers with the MeSH is incessantly growing and automated approaches are constantly evolving. Since 2013, the BioASQ challenge has been promoting those evolutions by proposing datasets and evaluation metrics. In this paper, we present our system, USI, and how we adapted it to participate to this challenge this year. USI is a generic approach, which means it does not directly take into account the content of the document to annotate. The results lead us to the conclusion that methods that solely rely on semantic annotations available in the corpus can already perform well compared to NLP-based approaches as our results always figure in the top ones.

1997
INDEXING AND RETRIEVAL OF THE MOST CHARACTERISTIC FRAMES / SCENES IN VIDEO DATABASES

An integrated framework for automatic extraction of the most characteristic frames or scenes of a video sequence is presented in this paper. This is accomplished by extracting a collection of a small number of frames or scenes that provide sufficient information about the video sequence. The scene/frame selection mechanism is based on a transformation from the image to a feature domain, which is more suitable for image comparisons, queries and retrieval.

2008 - 2008 Ninth International Workshop on Image Analysis for Multimedia Interactive Services
User Requirements for Multimedia Indexing and Retrieval of Unedited Audio-Visual Footage - RUSHES

Multimedia analysis and reuse of raw un-edited audio visual content known as rushes is gaining acceptance by a large number of research labs and companies. A set of research projects are considering multimedia indexing, annotation, search and retrieval in the context of European funded research, but only the FP6 project RUSHES is focusing on automatic semantic annotation, indexing and retrieval of raw and un-edited audio-visual content. Even professional content creators and providers as well as home-users are dealing with this type of content and therefore novel technologies for semantic search and retrieval are required. As a first result of this project, the user requirements and possible user-scenarios are presented in this paper. These results lay down the foundation for the research and development of a multimedia search engine particularly dedicated to the specific needs of the users and the content.

2010 - 2010 IEEE Fourth International Conference on Semantic Computing
A Joint Content-Event Model for Event-Centric Multimedia Indexing

In this paper, a joint content-event model for indexing multimedia data is proposed. The event part of the model follows a number of formal principles to represent several aspects of real-life events, whereas the content part is used to describe the decomposition of any type of multimedia data to content segments. In contrast to other event models for multimedia indexing, the proposed model treats events as first class entities and provides a referencing mechanism to link real-life event elements with content segments at multiple granularity levels. This referencing mechanism has been defined with the objective to facilitate the automatic enrichment of event elements with information extracted by automatic analysis of content segments, enabling event-centric multimedia indexing in large-scale multimedia collections.

2008 - IEEE Transactions on Multimedia
Gossip-Based Computation of a Gaussian Mixture Model for Distributed Multimedia Indexing

This paper deals with pattern recognition in a distributed computing context of the peer-to-peer type, that should be more and more interesting for multimedia data indexing and retrieval. Our goal is estimating of class-conditional probability densities, that take the form of Gaussian mixture models (GMM). Originally, we propagate GMMs in a decentralized fashion (gossip) in a network, and aggregate GMMs from various sources, through a technique that only involves little computation and that makes parsimonious usage of the network resource, as model parameters rather than data are transmitted. The aggregation is based on iterative optimization of an approximation of a KL divergence allowing closed-form computation between mixture models. Experimental results demonstrate the scheme to the case of speaker recognition.

2003
Support Vector Machines for Text Categorization Based on Latent Semantic Indexing

Text Categorization(TC) is an important component in many information organization and information management tasks. Two key issues in TC are feature coding and classifier design. In this paper Text Categorization via Support Vector Machines(SVMs) approach based on Latent Semantic Indexing(LSI) is described. Latent Semantic Indexing[1][2] is a method for selecting informative subspaces of feature spaces with the goal of obtaining a compact representation of document. Support Vector Machines[3] are powerful machine learning systems, which combine remarkable performance with an elegant theoretical framework. The SVMs well fits the Text Categorization task due to the special properties of text itself. Experiments show that the LSI+SVMs frame improves clustering performance by focusing attention of Support Vector Machines onto informative subspaces of the feature spaces.

2001 - Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205)
Region-based color image indexing and retrieval

A region-based color image indexing and retrieval algorithm is presented. As a basis for the indexing, a novel K-Means segmentation algorithm is used, modified so as to take into account the coherence of the regions. A new color distance is also defined for this algorithm. Based on the extracted regions, characteristic features are estimated using color, texture and shape information. An important and unique aspect of the algorithm is that, in the context of similarity-based querying, the user is allowed to view the internal representation of the submitted image and the query results. Experimental results demonstrate the performance of the algorithm. The development of an intelligent image content-based search engine for the World-Wide Web is also presented, as a direct application of the presented algorithm.

2003
Sparsification Strategies in Latent Semantic Indexing

The text retrieval method using Latent Semantic Indexing (LSI) with the truncated Singular Value Decomposition (SVD) has been intensively studied in recent years. The term-document matrices after SVD are full matrices, although the rank is reduced substantially. To reduce memory consumption, we examine some strategies to sparsify the truncated SVD matrices. After applying the sparsification strategies to three popular document databases, we find that some of our strategies not only sparsify the SVD matrices, but may also increase the accuracy of the text retrieval in some cases.

2011 - TRECVID
Quaero at TRECVID 2011: Semantic Indexing and Multimedia Event Detection

The Quaero group is a consortium of French and German organizations working on Multimedia Indexing and Retrieval. LIG and KIT participated to the semantic indexing task and LIG participated to the organization of this task. LIG also participated to the multimedia event detection task. This paper describes these participations. For the semantic indexing task, our approach uses a six-stages processing pipelines for computing scores for the likelihood of a video shot to contain a target concept. These scores are then used for producing a ranked list of images or shots that are the most likely to contain the target concept. The pipeline is composed of the following steps: descriptor extraction, descriptor optimization, classifi cation, fusion of descriptor variants, higher-level fusion, and re-ranking. We used a number of diff erent descriptors and a hierarchical fusion strategy. We also used conceptual feedback by adding a vector of classi fication score to the pool of descriptors. The best Quaero run has a Mean Inferred Average Precision of 0.1529, which ranked us 3rd out of 19 participants. We participated to the multimedia event detection task with a system derived from the generic one we have for general purpose concept indexing in videos considering the target events as concepts. Detection scores on videos are produced from the scores on shots.

1990 - ACM Sigois Bulletin
Using latent semantic indexing for information filtering

Latent Semantic Indexing (LSI) is an information retrieval method that organizes information into a semantic structure that takes advantage of some of the implicit higher-order associations of words with text objects. The resulting structure reflects the major associative patterns in the data while ignoring some of the smaller variations that may be due to idiosyncrasies in the word usage of individual documents. This permits retrieval based on the “latent” semantic content of the documents rather than just on keyword matches. This paper evaluates using LSI for filtering information such as Netnews articles based on a model of user preferences for articles. Users judged articles on how interesting they were and based on these judgements, LSI predicted whether new articles would be judged interesting. LSI improved prediction performance over keyword matching an average of 13% and showed a 26% improvement in precision over presenting articles in the order received. The results indicate that user preferences for articles tend to cluster based on the semantic similarities between articles.

2009 - Knowledge and Information
Folksonomies - Indexing and Retrieval in Web 2.0

Collaborative information services on Web 2.0 are used by Internet users not only to produce digital information resources, but also to furnish the contents of the resources with their own keywords, so-called tags. Whilst doing so the user is not required to comply with rules, as is necessary with a library catalogue. The amount of user-generated tags in a collaborative information service is referred to as folksonomy. Folksonomies allow users to relocate their own resources and to search for other resources. This book deals with collaborative information services and folksonomies both as a method of representing knowledge and a tool for information retrieval.

2016 - TRECVID
NII-HITACHI-UIT at TRECVID 2017

This paper presents the proposed system of our team for TRECVID Instance Search task. In this year system, we focus on person recognition step and scene tracking to improve both precision and recall of the system. First, instead of using face from the bottom of initial ranked list which is very weak in classification, we use face samples from the top of the ranked list which is the second highest score. Based on this strategy, the second highest score guarantee that the chosen samples are hard negative. For classification model, we also use SVM algorithm with RBF kernel instead of linear kernel. Last but not least, to improve the recall of the system, we track top returned shots using person reidentification methods. The final results show that, the proposed hard negative samples and scene tracking method help to improve performance of the system.

2016 - Multimedia Tools and Applications
TRECVid Semantic Indexing of Video: A 6-year Retrospective

Semantic indexing, or assigning semantic tags to video samples, is a key component for content-based access to video documents and collections. The Semantic Indexing task has been run at TRECVid from 2010 to 2015 with the support of NIST and the Quaero project. As with the previous High-Level Feature detection task which ran from 2002 to 2009, the semantic indexing task aims at evaluating methods and systems for detecting visual, auditory or multi-modal concepts in video shots. In addition to the main semantic indexing task, four secondary tasks were proposed namely the "localization" task, the "concept pair" task, the "no annotation" task, and the "progress" task. It attracted over 40 research teams during its running period. The task was conducted using a total of 1,400 hours of video data drawn from Internet Archive videos with Creative Commons licenses gathered by NIST. 200 hours of new test data was made available each year plus 200 more as development data in 2010. The number of target concepts to be detected started from 130 in 2010 and was extended to 346 in 2011. Both the increase in the volume of video data and in the number of target concepts favored the development of generic and scalable methods. Over 8 millions shots×concepts direct annotations plus over 20 millions indirect ones were produced by the participants and the Quaero project on a total of 800 hours of development data. Significant progress was accomplished during the period as this was accurately measured in the context of the progress task but also from some of the participants' contrast experiments. This paper describes the data, protocol and metrics used for the main and the secondary tasks, the results obtained and the main approaches used by participants.

2014 - CLEF
NCBI at the 2014 BioASQ Challenge Task: Large-scale Biomedical Semantic Indexing and Question Answering

In this paper we report our participation in the 2014 BioASQ chal- lenge tasks on biomedical semantic indexing and question answering. For the biomedical semantic indexing task (Task 2a) where participating teams are pro- vided with PubMed articles and asked to return relevant MeSH terms, we built on our previous learning-to-rank framework with a special focus on systemati- cally incorporating results of complementary methods for improved perfor- mance. For the question answering task (Task 2b) where teams are provided with natural language questions and asked to return responses in the format of documents, snippets, concepts and RDF triplets (Phase A) and direct answers (Phase B), we relied on PubMed search engines and our state-of-the-art named entity recognition tools such as DNorm and tmVar in Phases A and B, respec- tively. The official challenge results demonstrate that we consistently per- formed better than the baseline approaches for Task 2a and Task 2b (Phase B), and ranked among the top tier systems in the 2014 challenge.

2008 - ICVS
CEDD: Color and Edge Directivity Descriptor: A Compact Descriptor for Image Indexing and Retrieval

This paper deals with a new low level feature that is extracted from the images and can be used for indexing and retrieval. This feature is called "Color and Edge Directivity Descriptor" and incorporates color and texture information in a histogram. CEDD size is limited to 54 bytes per image, rendering this descriptor suitable for use in large image databases. One of the most important attribute of the CEDD is the low computational power needed for its extraction, in comparison with the needs of the most MPEG-7 descriptors. The objective measure called ANMRR is used to evaluate the performance of the proposed feature. An online demo that implements the proposed feature in an image retrieval system is available at: http://orpheus.ee.duth.gr/image_retrieval.

IRIM at TRECVID 2014: Semantic Indexing and Instance Search

Sabin Tiberius Strat
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Nicolas Ballas
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H. Jégou
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P. Gosselin
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B. Mérialdo
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M. Crucianu
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G. Quénot
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G. Chollet
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Joseph Razik
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H. Bredin
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S. Ayache
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Charles-Edmond Bichot
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H. Glotin
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Liming Chen
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D. Petrovska-Delacrétaz
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A. Stoian
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P. Lambert
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E. Dellandréa
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J. Delhumeau
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Chao Zhu
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M. Cord
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M. Redi
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Boris Mansencal
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H. Borgne
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J. Benois-Pineau
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A. Benoît
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Benjamin Labbé
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Yuxing Tang
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F. Thollard
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Ngoc-Trung Tran
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A. Shabou
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Bahjat Safadi
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N. Derbas
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Rémi Vieux
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Boyang Gao
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Abdelkader Hamadi
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S. Paris
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Miriam Redi
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Sébastion Paris

Abstract:The IRIM group is a consortium of French teams supported by the GDR ISIS and working on Multimedia Indexing and Retrieval. This paper describes its participation to the TRECVID 2014 semantic indexing (SIN) and instance search (INS) tasks. For the semantic indexing task, our approach uses a six-stages processing pipelines for computing scores for the likelihood of a video shot to contain a target concept. These scores are then used for producing a ranked list of images or shots that are the most likely to contain the target concept. The pipeline is composed of the following steps: descriptor extraction, descriptor optimization, classification, fusion of descriptor variants, higher-level fusion, and re-ranking. We evaluated a number of different descriptors and tried different fusion strategies. The best IRIM run has a Mean Inferred Average Precision of 0.2796, which ranked us 5th out of 15 participants.For INS 2014 task IRIM participation, the classical BoW approach was followed, trained only with east-enders dataset. Shot signatures were computed on one key frame, or several key frames (at 1fps) and average pooling. A dissimilarity, computing a distance only for words present in query, was tested. A saliency map, build from object ROI to incorporate background context, was tried. Late fusion of two individual BoWresults, with different detectors/descriptors (Hessian-Affine/SIFT and Harris-Laplace/Opponent SIFT), was used. The four submitted runs were the following:- Run F_D_IRIM_1 was the late fusion of BOW with SIFT, dissimilarity L2p, on several key frames per shot, with context for queries, and BOW with Opponent SIFT, dissimilarity L1p, on one key frame per shot.- Run F_D_IRIM_2 was similar to F_D_IRIM_1 but context for queries used also for second BoW.- Run F_D_IRIM_3 was similar to F_D_IRIM_1 but no context for queries used.- Run F_D_IRIM_4 was similar to F_D_IRIM_2 but using delta1 dissimilarity [46] (from INS 2013 best run).We found that extracting several key frames per shot coupled with average pooling improved results. We confirmed than including context in queries was also beneficial. Surprisingly, our dissimilarity performed better than delta1.

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引用
An Efficient Approach based on Image Pixel and Semantic Features Towards Video Retrieval
2018 2nd International Conference on Imaging, Signal Processing and Communication (ICISPC)
2018
HNIP: Compact Deep Invariant Representations for Video Matching, Localization, and Retrieval
IEEE Transactions on Multimedia
2017
Semantic Concept Detection Using Dense Codeword Motion
ACIVS
2013
Violence Detection in Video by Large Scale Multi-Scale Local Binary Pattern Dynamics
MediaEval
2012
Few-Shot Adaptation for Multimedia Semantic Indexing
ACM Multimedia
2018
Community-driven hierarchical fusion of numerous classifiers: Application to video semantic indexing
2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
2012
Hierarchical Late Fusion for Concept Detection in Videos
ECCV Workshops
2012
Large-Scale Query-by-Image Video Retrieval Using Bloom Filters
ArXiv
2016
Temporal aggregation for large-scale query-by-image video retrieval
2015 IEEE International Conference on Image Processing (ICIP)
2015
Multimodal Fusion: Combining Visual and Textual Cues for Concept Detection in Video
Multimedia Data Mining and Analytics
2015
The Stanford/Technicolor/Fraunhofer HHI Video Semantic Indexing System
TRECVID
2012
Detection of Multiple Instances of Video Objects
2011 Seventh International Conference on Signal Image Technology & Internet-Based Systems
2011
Retrieval of Multiple Instances of Objects in Videos
MMM
2012
CEA LIST at TRECVID 2012 : Semantic Indexing and instance search
TRECVID
2012
Retina-enhanced SURF descriptors for semantic concept detection in videos
2012 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA)
2012
Extraction of read text using a wearable eye tracker for automatic video annotation
UbiComp/ISWC Adjunct
2015
A factorized model for multiple SVM and multi-label classification for large scale multimedia indexing
2015 13th International Workshop on Content-Based Multimedia Indexing (CBMI)
2015
Hierarchical Hidden Markov Model in detecting activities of daily living in wearable videos for studies of dementia
Multimedia Tools and Applications
2011
Retina enhanced SIFT descriptors for video indexing
2013 11th International Workshop on Content-Based Multimedia Indexing (CBMI)
2013
VideoSense at TRECVID 2011: Semantic Indexing from Light Similarity Functions-based Domain Adaptation with Stacking
TRECVID
2011