H
Hongying Meng
Researcher at Brunel University London
Publications - 153
Citations - 4295
Hongying Meng is an academic researcher from Brunel University London. The author has contributed to research in topics: Computer science & Facial expression. The author has an hindex of 28, co-authored 132 publications receiving 3136 citations. Previous affiliations of Hongying Meng include University of York & University of Dundee.
Papers
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Book ChapterDOI
The 2005 PASCAL visual object classes challenge
Mark Everingham,Andrew Zisserman,Christopher Williams,Luc Van Gool,Moray Allan,Christopher M. Bishop,Olivier Chapelle,Navneet Dalal,Thomas Deselaers,Gyuri Dorkó,Stefan Duffner,J Eichhorn,Jason Farquhar,Mario Fritz,Christophe Garcia,Tom Griffiths,Frédéric Jurie,Daniel Keysers,Markus Koskela,Jorma Laaksonen,Diane Larlus,Bastian Leibe,Hongying Meng,Hermann Ney,Bernt Schiele,Cordelia Schmid,Edgar Seemann,John Shawe-Taylor,Amos Storkey,Sandor Szedmak,Bill Triggs,Ilkay Ulusoy,Ville Viitaniemi,Jianguo Zhang +33 more
TL;DR: The PASCAL Visual Object Classes Challenge (PASCALVOC) as mentioned in this paper was held from February to March 2005 to recognize objects from a number of visual object classes in realistic scenes (i.e. not pre-segmented objects).
Proceedings Article
Two view learning: SVM-2K, Theory and Practice
TL;DR: This paper proposes a method that combines this two stage learning (KCCA followed by SVM) into a single optimisation termed SVM-2K and presents both experimental and theoretical analysis of the approach showing encouraging results and insights.
Journal ArticleDOI
Significantly Fast and Robust Fuzzy C-Means Clustering Algorithm Based on Morphological Reconstruction and Membership Filtering
TL;DR: An improved FCM algorithm based on morphological reconstruction and membership filtering (FRFCM) that is significantly faster and more robust than FCM is proposed in this paper and demonstrates that the proposed algorithm not only achieves better results, but also requires less time than the state-of-the-art algorithms for image segmentation.
Journal ArticleDOI
Superpixel-Based Fast Fuzzy C-Means Clustering for Color Image Segmentation
TL;DR: A superpixel-based fast FCM clustering algorithm that is significantly faster and more robust than state-of-the-art clustering algorithms for color image segmentation and implemented with histogram parameter on the superpixel image is proposed.
Proceedings ArticleDOI
Depression recognition based on dynamic facial and vocal expression features using partial least square regression
TL;DR: A novel method is presented, which comprehensively models visual and vocal modalities, and automatically predicts the scale of depression, and experimental results clearly highlight its effectiveness and better performance than baseline results provided by the AVEC2013 challenge organiser.