2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDMLSP-38.3
Paper Title DHCN: DEEP HIERARCHICAL CONTEXT NETWORKS FOR IMAGE ANNOTATION
Authors Mingyuan Jiu, Zhengzhou University, China; Hichem Sahbi, CNRS Sorbonne University, France
SessionMLSP-38: Neural Networks for Clustering and Classification
LocationGather.Town
Session Time:Thursday, 10 June, 16:30 - 17:15
Presentation Time:Thursday, 10 June, 16:30 - 17:15
Presentation Poster
Topic Machine Learning for Signal Processing: [MLR-PRCL] Pattern recognition and classification
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Context modeling is one of the most fertile subfields of visual recognition which aims at designing discriminant image representations while incorporating their intrinsic and extrinsic relationships. However, the potential of context modeling is currently under-explored and most of the existing solutions are either context-free or restricted to simple handcrafted geometric relationships. We introduce in this paper DHCN: a novel Deep Hierarchical Context Network that leverages different sources of contexts including geometric and semantic relationships. The proposed method is based on the minimization of an objective function mixing a fidelity term, a context criterion and a regularizer. The solution of this objective function defines the architecture of a bi-level hierarchical context network; the first level of this network captures scene geometry while the second one corresponds to semantic relationships. We solve this representation learning problem by training its underlying deep network whose parameters correspond to the most influencing bi-level contextual relationships and we evaluate its performances on image annotation using the challenging ImageCLEF benchmark.