Optimal linear representations of images for object recognition
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Updated: Aug 21, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
Published on: December 15, 2023
Xiuwen Liu1, Anuj Srivastava, Kyle Gallivan
1Department of Computer Science, Florida State University, Tallahassee, FL 32306, USA. liux@cs.fsu.edu
Abstract:
Although linear representations are frequently used in image analysis, their performances are seldom optimal in specific applications. This paper proposes a stochastic gradient algorithm for finding optimal linear representations of images for use in appearance-based object recognition. Using the nearest neighbor classifier, a recognition performance function is specified and linear representations that maximize this performance are sought. For solving this optimization problem on a Grassmann manifold, a stochastic gradient algorithm utilizing intrinsic flows is introduced. Several experimental results are presented to demonstrate this algorithm.
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