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Online Early-Late Fusion Based on Adaptive HMM for Sign Language Recognition by Dan Guo; Wengang Zhou; Houqiang Li; Meng Wang is a Computer Science article available to read on EtoBox.

What is Online Early-Late Fusion Based on Adaptive HMM for Sign Language Recognition about?

In sign language recognition (SLR) with multimodal data, a sign word can be represented by multiply features, for which there exist an intrinsic property and a mutually complementary relationship among them. To fully explore those relationships, we propose an online early-late fusion method based on the adaptive Hidden Markov Model (HMM). In terms of the intrinsic property, we discover that inherent latent change states of each sign are related not only to the number of key gestures and body poses but also to their translation relationships. We propose an adaptive HMM method to obtain the hidden state number of each sign by affinity propagation clustering. For the complementary relationship, we propose an online early-late fusion scheme. The early fusion (feature fusion) is dedicated to preserving useful information to achieve a better complementary score, while the late fusion (score fusion) uncovers the significance of those features and aggregates them in a weighting manner. Different from classical fusion methods, the fusion is query adaptive. For different queries, after feature selection (including the combined feature), the fusion weight is inversely proportional to the area

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Author

Dan Guo; Wengang Zhou; Houqiang Li; Meng Wang

Publisher

ACM

Published

2017

Language

EN

Field

Computer Science (Physical Sciences)