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Elisa Fromont, Kick-off meeting, 14/02/2014

SoLSTiCe Similarity of locally structured data in computer vision Université -Jean Monnet (Saint-Etienne) LIRIS (Lyon) (1/02/2014 -2018). Elisa Fromont, Kick-off meeting, 14/02/2014. Présentation du consortium. Main ideas.

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Elisa Fromont, Kick-off meeting, 14/02/2014

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  1. SoLSTiCeSimilarity of locally structured data in computer visionUniversité-Jean Monnet (Saint-Etienne)LIRIS (Lyon)(1/02/2014 -2018) Elisa Fromont, Kick-off meeting, 14/02/2014

  2. Présentation du consortium

  3. Main ideas • Aim: design new models and tools for representing and managing images and videos • Targeted applications: classification, recognition or indexing (in a context of occlusions and non rigid objects in 2D (+ t), 3D and 3D+t media) • Proposal: explore locally structured data (LSD) = visual features + discrete structures to model local (spatio-temporal) relationships • 3 main tasks: • [Extracting LSD from images and videos:] extract relevant visual features and structure them w.r.t. spatial and temporal relationships. • [Measuring the similarity of LSD:] design relevant similarity measures for comparing LSD, and efficient algorithms for computing these measures. • [Mining LSD:] characterize LSD by means of frequently (or infrequently) occurring patterns (itemsets, sequences or graphs) and use them to create discriminative features for solving computer vision tasks.

  4. The project : 4 tasks interconnected • [Task 0] will be dedicated to the project management; • [Task 1] will design LSD for describing images and videos, andwill design tools for extracting these LSD; • [Task 2] will design kernels, similarity measures and matchingalgorithms for comparing LSD; • [Task 3] will design mining algorithms for extracting relevantpatterns in LSD; • [Task 4] will be dedicated to the design and use of demo platforms to test (and demonstrate) on computer vision benchmarks and new datasets the models and tools designed in Tasks 1 to 3.

  5. Livrables (1/2)

  6. Livrables (2/2)

  7. Planning

  8. Valorisation/Impact • scientific communications submitted to major conferences and journals (CVPR, ECCV, ICCV, ICPR, AVSS, KDD, ICML, ECML, PKDD, ICPR, etc.) and journals (IEEE-T-PAMI, PR, IJCV, CVIU, MLJ, JMLR, etc.) in image processing, pattern recognition, combinatorial optimization, machine learning, and data mining. • open source platforms developed in task 4 (and task 2) • workshops co-located with major conferences in order to share ongoing research. • design educational and recreational demos targeting a non specialist public to be presented during popular events such as “la fête de la science”.

  9. Use of resources • LaHC (140000 euros): • Staff (100000 euros) Ph.D Student: 36 months on « New matching strategies for data mining applied to computer vision problems » (tasks 2 and 3 + 1 and 4) co-supervised with liris • Travels • Other expenses: master thesis grants + hardware • LIRIS (134000 euros) • Staff (100000 euros): Ph.D Student: 36 months on « Analysis of complex scenes with structured models » (tasks 1 and 2 + 3) co-supervised with LaHC • Travels • Other expenses: master thesis grants + hardware

  10. Points to discuss • Website (Jean Monnet) • Includesome more members (Taygun, Romain?) • How to spend the money for the second thesis (Remi, Marc, Damien ?) • Demos? • Next meetings

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