Academia.eduAcademia.edu

Outline

Title
Abstract
Introduction
Instances Examples and Data Sets
Search Methods
Results
Discussion
References

IRIM at TRECVID 2011: Semantic Indexing and Instance Search

Profile image of Jenny Benois-pineauJenny Benois-pineau

2011

Abstract

The IRIM group is a consortium of French teams working on Multimedia Indexing and Retrieval. This paper describes its participation to the TRECVID 2011 semantic indexing and instance search 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 0.1387, which ranked us 5th out of 19 participants. For the instance search task, we we used both object based query and frame based query. We formulated the query in standard way as comparison of visual signatures either of object with parts of DB frames or as a comparison of visual signatures of query and DB frames. To produce visual signatures we also used two apporaches: the first one is the baseline Bag-Of-Visual-Words (BOVW) model based on SURF interest point descriptor; the second approach is a Bag-Of-Regions (BOR) model that extends the traditional notion of BOVW vocabulary not only to keypoint-based descriptors but to region based descriptors.

Related papers

Experimenting vireo-374: Bag-of-visual-words and visual-based ontology for semantic video indexing and search
Hung Khoon Tan

TREC Video Retrieval Evaluation Proceedings, 2007

In this paper, we present our approaches and results of high-level feature extraction and automatic video search in TRECVID-2007. In high-level feature extraction, our main focus is to explore the upper limit of bag-of-visualwords (BoW) approach based upon local appearance features. We study and evaluate several factors which could impact the performance of BoW. By considering these important factors, we show that a local feature only system already yields top performance (MAP = 0.0935

View PDFchevron_right
IRIM at TRECVID 2015: Semantic Indexing
alexandre benoit

2015

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 2015 semantic indexing (SIN). 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 0.2947, which ranked it 4th out of 15 participants.

View PDFchevron_right
IRIM at TRECVID 2010 : Semantic Indexing and Instance Search
Boris Mansencal, Patrick Lambert

2010

The IRIM group is a consortium of French teams working on Multimedia Indexing and Retrieval. This paper describes our participation to the TRECVID 2010 semantic indexing and instance search tasks. For the semantic indexing task, we evaluated a number of different descriptors and tried different fusion strategies, in particular hierarchical fusion. The best IRIM run has a Mean Inferred Average Precision of 0.0442, which is above the task median performance. We found that fusion of the classification scores from different classifier types improves the performance and that even with a quite low individual performance, audio descriptors can help. For the instance search task, we used only one of the example images in our queries. The rank is nearly in the middle of the list of participants. The experiment showed that HSV features outperform the concatenation of HSV and Edge histograms or the Wavelet features.

View PDFchevron_right

References (25)

  1. A. Smeaton, P. Over and W. Kraaij, Evaluation cam- paigns and TRECVid, In MIR'06: Proceedings of the 8th ACM International Workshop on Multimedia Infor- mation Retrieval, pp321-330, 2006.
  2. P. Over, G. Awad, J. , B. Antonishek, M.2Michel, A. Smeaton, W. Kraaij, and G. Quénot, TRECVID 2011 -An Overview of the Goals, Tasks, Data, Eval- uation Mechanisms, and Metrics In Proceedings of the TRECVID 2011 workshop, Gaithersburg, USA, 5-7 Dec. 2011.
  3. Y.-C. Cheng and S.-Y. Chen. Image classification using color, texture and regions. In Image and Vision Com- puting, 21:759-776, 2003.
  4. P.H. Gosselin, M. Cord, Sylvie Philipp-Foliguet. Com- bining visual dictionary, kernel-based similarity and learning strategy for image category retrieval. In Com- puter Vision and Image Understanding, Special Issue on Similarity Matching in Computer Vision and Multi- media. Volume 110, Issue 3, Pages 403-41, 2008.
  5. D. Gorisse, M. Cord, F. Precioso, SALSAS: Sub-linear active learning strategy with approximate k-NN search, Pattern Recognition, In Press, Corrected Proof, Avail- able online 21 December 2010.
  6. M. Redi and B. Merialdo, Saliency moments for image categorization, In ICMR 2011, 1st ACM International Conference on Multimedia Retrieval, April 17-20, 2011, Trento, Italy.
  7. A. Oliva and A. Torralba, Modeling the shape of the scene: A holistic representation of the spatial envelope, In International Journal of Computer Vision, vol 42, number 3, pages 145-175, 2001.
  8. K. E. A. van de Sande, T. Gevers, and C. G. M. Snoek. A comparison of color features for visual concept clas- sification. In ACM International Conference on Image and Video Retrieval, pages 141-150, 2008.
  9. Ivan Laptev, On space-time interest points, Int. J. Comput. Vision, 64:107-123, September 2005.
  10. A. Benoit, A. Caplier, B. Durette, and J. Herault, Us- ing human visual system modeling for bio-inspired low level image processing, In Computer Vision and Image Understanding, vol. 114, no. 7, pp. 758-773, 2010.
  11. S. Paris, H .Glotin, Pyramidal Multi-level Features for the Robot Vision@ICPR 2010 Challenge, In 20th Inter- national Conference on Pattern Recognition, pp.2949- 2952, 2010
  12. B. Safadi, G. Quénot. Evaluations of multi-learners approaches for concepts indexing in video documents. In RIAO, Paris, France, April 2010.
  13. Georges Quénot. KNNLSB: K Nearest Neighbors Linear Scan Baseline, 2008. Software available at http://mrim.imag.fr/georges.quenot/freesoft/ knnlsb/index.html.
  14. Safadi et al. Quaero at TRECVID 2011: Semantic In- dexing and Multimedia Event Detection, In Proceedings of the TRECVID 2011 workshop, Gaithersburg, USA, 5-7 Dec. 2011.
  15. Stéphane Ayache and Georges Quénot, Video Corpus Annotation using Active Learning, In 30th European Conference on Information Retrieval (ECIR'08), Glas- gow, Scotland, 30th March -3rd April, 2008.
  16. D. Gorisse et al., IRIM at TRECVID 2010: High Level Feature Extraction and Instance Search. In TREC Video Retrieval Evaluation workshop, Gaithers- burg, MD USA, November 2010.
  17. Alice Porebski, Color texture feature selection for im- age classification. Application to flaw identification on decorated glasses printing by a silk-screen process. Phd thesis, Universit Lille 1, Sciences et Technologies, Nov. 2009
  18. V. D. Blondel and J. Guillaume and R. Lambiotte and E. Lefebvre, Fast Unfolding of Community Hierarchies in Large Networks, In Computing Research Repository, abs/0803.0, 2008.
  19. B. Safadi, G. Qunot. Re-ranking by Local Re-scoring for Video Indexing and Retrieval, CIKM 2011: 20th ACM Conference on Information and Knowledge Man- agement, Glasgow, Scotland, oct 2011.
  20. J. Sivic and A. Zisserman. Video google: a text retrieval approach to object matching in videos. In ICCV'03, volume 2, pages 1470-1477, 2003.
  21. H. Bay, Herbert, T.Tuytelaars,and L. Van Gool. SURF: Speeded Up Robust Features, In ECCV 2006, pp 404-417, 2006.
  22. R. Vieux, J. Benois-Pineau, and J.-Ph. Domenger. Content based image retrieval using bag of region. In MMM 2012 -The 18th International Conference on Multimedia Modeling, 2012.
  23. P. F. Felzenszwalb and D. P. Huttenlocher. Efficient graph-based image segmentation. International Journal of Computer Vision, 59:167-181, 2004.
  24. Emilie Dumont and Bernard Merialdo. Rushes video summarization and evaluation. Multimedia Tools and Applications, Springer, Vol.48, N1, May 2010, 2010.
  25. Edwin Lughofer. Extensions of vector quantization for incremental clustering. Pattern Recognition, 41:995- 1011, 2008.

Related papers

IRIM at TRECVID 2013 : Semantic Indexing and Instance Search
Boris Mansencal

TREC Video Retrieval Evaluation, 2013

The IRIM group is a consortium of French teams working on Multimedia Indexing and Retrieval. This paper describes its participation to the TRECVID 2013 semantic indexing and instance search 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 4th out of 26 participants.

View PDFchevron_right
IRIM at TRECVID 2014: Semantic Indexing and Instance Search
Boris Mansencal

2014

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.

View PDFchevron_right
Author manuscript, published in "Proc. TRECVID Workshop (2013)" IRIM at TRECVID 2013: Semantic Indexing and Instance Search
Boris Mansencal

2014

The IRIM group is a consortium of French teams working on Multimedia Indexing and Retrieval. This paper describes its participation to the TRECVID 2013 semantic indexing and instance search 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 4th out of 26 participants.

View PDFchevron_right
IRIM at TRECVID 2012: Semantic Indexing and Instance Search
Alexandre Benoit, Patrick Lambert

2012

View PDFchevron_right
Quaero at TRECVID 2013: Semantic Indexing and Instance Search
Philippe Mulhem

The Quaero group is a consortium of French and German organizations working on Multimedia Indexing and Retrieval 1. LIG participated to the semantic indexing main task, localization task and concept pair task. LIG also participated to the organization of this task. This paper describes these participations which are quite similar to our previous year's participations. For the semantic indexing main 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 used a number of different descriptors and a hierarchical fusion strategy. We also used conceptual feedback by adding a vector of classification score to the pool of descriptors. The best Quaero run has a Mean Inferred Average Precision of 0.2848, which ranked us 2 nd out of 26 participants. We also co-organized the TRECVid SIN 2013 task and collaborative annotation.

View PDFchevron_right
580 California St., Suite 400
San Francisco, CA, 94104
© 2025 Academia. All rights reserved