Spatiotemporal Gabor filters: a new method for dynamic texture recognition

TL;DR

This paper introduces a novel dynamic texture recognition method using spatiotemporal Gabor filters, which effectively captures motion and appearance features, demonstrating robustness on challenging datasets.

Contribution

The paper is the first to apply spatiotemporal Gabor filters for dynamic texture recognition, providing a new approach that models both spatial and temporal texture features.

Findings

Effective recognition on challenging databases

Robustness demonstrated through experimental results

First successful application of spatiotemporal Gabor filters in this domain

Abstract

This paper presents a new method for dynamic texture recognition based on spatiotemporal Gabor filters. Dynamic textures have emerged as a new field of investigation that extends the concept of self-similarity of texture image to the spatiotemporal domain. To model a dynamic texture, we convolve the sequence of images to a bank of spatiotemporal Gabor filters. For each response, a feature vector is built by calculating the energy statistic. As far as the authors know, this paper is the first to report an effective method for dynamic texture recognition using spatiotemporal Gabor filters. We evaluate the proposed method on two challenging databases and the experimental results indicate that the proposed method is a robust approach for dynamic texture recognition.