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Shared-Neighbours methods for visual content structuring and mining

Abstract : This thesis investigates new clustering paradigms and algorithms based on the principle of the shared nearest-neighbors (SNN. As most other graph-based clustering approaches, SNN methods are actually well suited to overcome data complexity, heterogeneity and high-dimensionality.The first contribution of the thesis is to revisit existing shared neighbors methods in two points. We first introduce a new SNN formalism based on the theory of a contrario decision. This allows us to derive more reliable connectivity scores of candidate clusters and a more intuitive interpretation of locally optimum neighborhoods. We also propose a new factorization algorithm for speeding-up the intensive computation of the required sharedneighbors matrices.The second contribution of the thesis is a generalization of the SNN clustering approach to the multi-source case. Whereas SNN methods appear to be ideally suited to sets of heterogeneous information sources, this multi-source problem was surprisingly not addressed in the literature beforehand. The main originality of our approach is that we introduce an information source selection step in the computation of candidate cluster scores. As shown in the experiments, this source selection step makes our approach widely robust to the presence of locally outlier sources. This new method is applied to a wide range of problems including multimodal structuring of image collections and subspace-based clustering based on random projections. The third contribution of the thesis is an attempt to extend SNN methods to the context of bipartite k-nn graphs. We introduce new SNN relevance measures revisited for this asymmetric context and show that they can be used to select locally optimal bi-partite clusters. Accordingly, we propose a new bipartite SNN clustering algorithm that is applied to visual object’s discovery based on a randomly precomputed matching graph. Experiments show that this new method outperformed state-of-the-art object mining results on OxfordBuilding dataset. Based on the discovered objects, we also introduce a new visual search paradigm, i.e. object-based visual query suggestion.
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  • HAL Id : tel-00856582, version 1



Amel Hamzaoui. Shared-Neighbours methods for visual content structuring and mining. Other [cs.OH]. Université Paris Sud - Paris XI, 2012. English. ⟨NNT : 2012PA112079⟩. ⟨tel-00856582⟩



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