Abstract
Video based object recognition and classification has been widely studied in computer vision and image processing area. One main issue of this task is to develop an effective representation for video. This problem can generally be formulated as image set representation. In this paper, we present a new method called Multiple Covariance Discriminative Learning (MCDL) for image set representation and classification problem. The core idea of MCDL is to represent an image set using multiple covariance matrices with each covariance matrix representing one cluster of images. Firstly, we use the Nonnegative Matrix Factorization (NMF) method to do image clustering within each image set, and then adopt Covariance Discriminative Learning on each cluster (subset) of images. At last, we adopt KLDA and nearest neighborhood classification method for image set classification. Promising experimental results on several datasets show the effectiveness of our MCDL method.
Highlights
With the recent development in imaging techniques, multiple images of an object are usually available in many cases, such as video based surveillance, multi-view camera networks, etc
Object recognition from these multiple images is formulated as an image set classification problem and has attracted more and more interests and attention in computer vision and machine learning area in recent years [1, 2, 3, 4, 5, 6, 7]
We present a new image set representation method called multiple covariance discriminative learning (MCDL), which aims to represent an image set using multiple covariance matrices with each covariance matrix representing one cluster of images
Summary
With the recent development in imaging techniques, multiple images of an object are usually available in many cases, such as video based surveillance, multi-view camera networks, etc. We present a new image set representation method called multiple covariance discriminative learning (MCDL), which aims to represent an image set using multiple covariance matrices with each covariance matrix representing one cluster of images. Comparing with previous single CDL method [5], MCDL explores the data distribution of multiple subspaces more providing a more faithful representation. Note that covariance-based visual representation has been used in many applications [22, 23] Different from these works, here we focus on multiple covariance matrices representation, which considers multi-subspaces property of image set data and providing a more effective descriptor for image set data. In the materials and methods part, we introduce nonnegative matrix factorization (NMF) data clustering method and propose our Multiple Covariance Matrices representation and Kernel LDA classification method. We apply MCDL method to some datasets to evaluate the effectiveness of the method
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