- Is the sparsity assumption always supported by the data? Nowadays, compressive sensing has become one of the standard techniques of object recognition. If the sparsity is however not supported by the data, it is not guarantee to recover the exact signal and therefore sparse approximations may not deliver the robustness or performance desired . In this case what sort of acceptable (in terms of computation load) robust method can be?
- When the Sparse Representation is Relevant? It is important here to perform an in-depth analysis of sparse representation in pattern recognition and see empirically if this sparse representation improves recognition performance compared to non-sparse representations . To this end, it would be important to take into consideration the way to extract features and to refine them as well as the computational load induced by the all process of sparse representation.
- Sparse Representation or Collaborative Representation, which one is the best? Similarly to Zhang’s work , it would be appropriate to see if the use of all training samples to collaboratively represent a query sample is much more crucial to sparse representation based classification (SRC). Taking into consideration the fact that the collaborative representation based classification (CRC) plays a more important role than L1-regularization as shown by Zhang et al. , it would be opportune to see what new instantiations of CRC (with less computational load than usual SRC) can be proposed.
- Is sparse representation and learning usefulness in the context of video-based action modeling and recognition? Are ideas from this application fairly general and applicable to other recognition problems? One should here explore the usefulness of sparse representation and learning in the context of video classification, looking particularly at the problem of recognizing human actions-both physical actions and facial expressions . This can be achieved by constructing an overcomplete dictionary using a set of spatio-temporal descriptors (extracted from the video sequences) in such a way that each of these descriptors is represented by some linear combination of a small number of dictionary elements. By doing so, one can achieve a more compact and richer representation than classical methods using clustering and vector quantization. It is also important to see which representation (sparse vs collaborative) is the more convenient for human-action recognition. Experiments and validation of generalization to other recognition problems can be done on several data sets containing various physical actions, facial expressions and object recognition.
- Supervisors: Pr. Brahim Chaib-draa
- Place: Department Computer Science and software Engineering, Laval University, Québec
- The PhD candidate will work at Damas lab part of the Department Computer Science and software Engineering in collaboration with researchers from REPARTI center of
which Chaib-draa is affiliated (http://reparti.gel.ulaval.ca/
). REPARTI provides several advantages, including a human infrastructure to help with some management aspects for research, and access to a wide network of other researchers in areas closely related to the topic of this PhD proposal.
- Duration: 36 months – starting date: September 2014, 1st
- Salary: 19 000$/Year + 3 000$/year (from University)
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