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Graph Embedding Multi-Kernel Metric Learning for Image Set Classification With Grassmannian Manifold-Valued Features
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DOI：10.1109/TMM.2020.2981189.png)
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In the domain of video-based image set classification, a considerable advance has been made by modeling a sequence of video frames (image set) as a linear subspace, which typically resides on a Grassmannian manifold. As a consequence of the large intra-class variations of the video data, there are two open challenges for the modeling task: how to establish appropriate image set models to encode these variations, and how to effectively measure the similarity between any two image sets. As a possible way to tackle these issues, this paper presents a graph embedding multi-kernel metric learning (GEMKML) algorithm for image set classification. The proposed GEMKML implements set modeling, feature extraction, and classification in two steps. Firstly, the proposed framework constructs a novel cascaded feature learning architecture on Grassmannian manifold with the aim of producing more effective Grassmannian manifold-valued feature representations. To make a better use of these learned features, a graph embedding multi-kernel metric learning scheme is then devised to map them into a lower-dimensional Euclidean space, where the inter-class distances are maximized and the intra-class distances are minimized. We evaluate the proposed GEMKML on five different visual classification tasks using widely adopted datasets. The extensive classification results confirm its superiority over the state-of-the-art methods.
Keyword:
Manifolds
Measurement
Feature extraction
Kernel
Geometry
Emotion recognition
Data models
Image set classification
Grassmannian manifold
Feature extraction
Graph embedding multi-kernel metric learning
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