Dynamic Texture Recognition via Nuclear Distances on Kernelized Scattering Histogram Spaces

TL;DR

This paper introduces a novel dynamic texture recognition method using nuclear distances on kernelized scattering histogram spaces, achieving state-of-the-art classification performance by focusing on frame-wise features and basis-invariant metrics.

Contribution

It proposes a new framework that describes dynamic textures with kernelized scattering histogram spaces and applies nuclear distances for improved recognition accuracy.

Findings

Competitive results for nearest neighbor classification

State-of-the-art results for nearest class center classification

Effective use of frame-wise features with basis-invariant metrics

Abstract

Distance-based dynamic texture recognition is an important research field in multimedia processing with applications ranging from retrieval to segmentation of video data. Based on the conjecture that the most distinctive characteristic of a dynamic texture is the appearance of its individual frames, this work proposes to describe dynamic textures as kernelized spaces of frame-wise feature vectors computed using the Scattering transform. By combining these spaces with a basis-invariant metric, we get a framework that produces competitive results for nearest neighbor classification and state-of-the-art results for nearest class center classification.