Informative Feature Selection for Object Recognition via Sparse PCA

Bag-of-words (BoW) methods are a popular class of object recognition methods that use image features (e.g., SIFT) to form visual dictionaries and subsequent histogram vectors to represent object images in the recognition process. The accuracy of the BoW classifiers, however, is often limited by the presence of uninformative features extracted from the background or irrelevant image segments. Most existing solutions to prune out uninformative features rely on enforcing pairwise epipolar geometry via an expensive structure-from-motion (SfM) procedure. Such solutions are known to break down easily when the camera transformation is large or when the features are extracted from lowresolution, low-quality images. In this paper, we propose a novel method to select informative object features using a more efficient algorithm called Sparse PCA. First, we show that using a large-scale multiple-view object database, informative features can be reliably identified from a highdimensional visual...

• Type: Journal
• Year: 2011
• Where: ICCV
• Authors: Nikhil Naikal, Allen Y. Yang, S. Shankar Sastry
• Keywords: Computer Vision, Dimensional Subspace, ICCV 2011, Lagrangian Method, Principal Vectors