Introduction

Jos¶e M. Perea-Ortega, Arturo Montejo-R¶aez, M. Teresa Mart¶³n-Valdivia, L. Alfonso Uren~a-L¶opez SINAI research group. Computer Science Department. University of Ja¶en Campus Las Lagunillas, Ed. A3, E-23071, Ja¶en, Spain

0 Campus Las Lagunillas , Ed. A3, E-23071, Ja 1 SINAI research group. Computer Science Department. University of Ja

2009

5152

This paper describes the second participation of the SINAI research group in the VideoCLEF track. This year we only participated in the subject classi¯cation task. A training collection was generated using the data provided by the VideoCLEF organization. Over this data, a supervised learning approach to classify the test videos was conducted. We have used Support Vector Machines (SVM) as classi¯cation algorithm and two experiments have been submitted, using the metadata ¯les and without using them, during the generation of the training corpus. The results obtained show the expected increase in precision due to the use of metadata in the classi¯cation of the test videos.

Image classi¯cation Information Retrieval

Introduction

This paper presents the second participation of the SINAI research group at the VideoCLEF 2009 track. The goal of the track is to develop and evaluate tasks involving the analysis of multilingual video content [ 6 ]. This year we only participate in the subject classi¯cation task. It is about automatic tagging of videos with subject labels such as \Archeology ", \Dance", \History ", \Music" or \Scienti¯c Research". A total of 46 subject labels have been de¯ned. The classi¯cation process only makes use of the speech transcriptions of the videos and some metadata provided.

Our group have some experience in the ¯eld of the multimedia video retrieval [ 4 ] and image retrieval, participating the last years in several tasks of the ImageCLEF track [ 3, 2, 1 ]. With regard to the video categorization, we participated in VideoCLEF 2008, applying a simple approach to resolve the classi¯cation task: to use an Information Retrieval (IR) system as classi¯er. The speech transcriptions were used as textual queries and we generated a search collection based on documents retrieved using the Google1 search engine. The results obtained showed that an IR system can perform well as video classi¯er if the speech transcriptions of the videos have good quality [ 8 ].

This year we have submited some experiments following one main approach: supervised categorization using labeled samples. For that, a learning corpus has been generated using the data provided by the VideoCLEF organization. Then, we have applied Support Vector Machines (SVM) [ 5 ] as classi¯cation algorithm.

The following section describes how the training collection has been generated. In Section 3, we explain brie°y the use of SVM as text classi¯er. In Section 4, we describe the experiments and we show the results. Finally, conclusions are presented in Section 5.

2 Generating the training corpus

The VideoCLEF 2009 training corpus consists of 262 XML ¯les. These Automatic Speech Recognition (ASR) ¯les belong to the VideoCLEF 2008 (50 ¯les) and TRECVid2 2007 (212 ¯les). In addition, there are some metadata ¯les provided by the VideoCLEF organization. A fragment of a ASR ¯le and a metadata ¯le are showed in Figure 1 and Figure 2, respectively. [...] <AudioSegment> <TextAnnotation>

<FreeTextAnnotation>gebouw</FreeTextAnnotation> </TextAnnotation> [...] [...] <asset> <assets_id>17785</assets_id> <title>NOORDERLICHT (expr_id:22205)</title> <creator>VPRO</creator> <creator>Dijk, Jochgem van</creator> <description>BG_34978-out.wmv</description> <description>Teleblik</description> <description>INTERVIEWS met: prof. Robert Behringer, fysicus

Duke University, (op het terrein van een cementfabriek) over het verrassend gedrag van korrelstructuren, hoe deze zich manifesteren als vaste stof maar ook als vloeistof of gas; de geschiedenis van het onderzoek; nieuwe onderzoeksimpulsen door het model van Per Bak en de chaostheorie; hoe men alleen de complexiteit begrijpt en worstelt om een goede fundamentele theorie op te stellen; dr. Eric Clement, fysicus Universita Pierre et Marie Curie, toont een aantal proeven waarbij wiskundige vormen in korrelstructuren ontstaan oa die laat zien hoe moeilijk het is korrels van verschillende grootte met elkaar te mengen, vergelijkt zijn proeven met die van Faraday en vertelt over de oorzaken van de wiskundige vormen en hoe moeilijk het in de industrie is met korrelstructuren te werken oa het mengen bij de aanmaak van beton.

Parijs: Clement op scooter door stad, bij cementfabriek en igm collega op werk; cementfabriek; souvenirdoosje met dwarrelsneeuw; zandloper wordt omgedraaid en loopt, vallend zand; </description> <description_abstract>Wetenschappelijk magazine met uiteenlopende onderwerpen. Programma met reportages over wetenschappelijke onderwerpen. Deze aflevering gaat over het zoeken naar natuurkundige wetmatigheden in korrelstructuren zoals zand. </description_abstract> <publisher>VPRO</publisher> [...] bels, generating a TREC ¯le per document. Besides, we have added the content of the description abstract labels from the metadata ¯les. The collection of training documents was processed ¯ltering the stop words and applying a stemmer. Because all the original ¯les are in Dutch language, we have used the Snowball stop word list for Dutch3, which contains 101 stop words, and the Snowball Dutch stemmer4.

3 Using SVM as text classi¯er

Automatic tagging of videos with subject labels can be seen as a categorization problem, using the speech transcriptions of the test videos like the documents to classify. One of the successful uses of SVM algorithms is the task of text categorization into ¯xed number of prede¯ned categories based on their content. Commonly utilized representation of text documents from the ¯eld of

Experiment

Using metadata Without using metadata information retrieval (IR) provides a natural mapping for construction of Mercer kernels utilized in SVM algorithms.

For the experiments and analysis carried out in this paper, the Rapid Miner5 framework was selected. This toolkit provides several machine learning algorithms such as SVM and techniques along with other interesting features. 4

Experiments and results

The experiments carried out in this paper are a ¯rst approximation to the automatic tagging of videos using a text classi¯er. Two experiments have been submited: using the metadata ¯les provided by the VideoCLEF organization and without using them, during the generation of the training corpus. The results obtained are showed in Table 1.

In order to evaluate the quality of the results, we have used two usual measures: the Mean Average Precision (MAP) and the R-precision. Analyzing the results, we can see that the use of metadata during the generation of the training corpus improves about 21.7% the average precision of the classi¯cation of the test videos. 5

Conclusions

The use of metadata as an valuable source of information in text categorization has been already applied some time ago, for example, in the categorization of full-text papers enriched by its bibliographic records [ 7 ].

We expect to continue this work by applying a multi-label classi¯er, instead the multiclass SVM algorithm used so far.

Acknowledgements

This work has been supported by the Regional Government of Andaluc¶³a (Spain) under excellence project GeOasis (P08-41999), under project on Tourism (FFIEXP06-TU2301-2007/000024), the Spanish Government under project Text-Mess TIMOM (TIN2006-15265-C06-03) and the local project RFC/PP2008/UJA-08-16-14. 5Rapid Miner is available from http://rapid-i.com/

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