Image sequence analysis using dynamic topic model

Amit Bhatia; Neil Bomberger

doi:10.1117/12.3016281

7 June 2024 Image sequence analysis using dynamic topic model

Amit Bhatia, Neil Bomberger

Proceedings Volume 13057, Signal Processing, Sensor/Information Fusion, and Target Recognition XXXIII; 130570R (2024) https://doi.org/10.1117/12.3016281
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

A topic model is a probabilistic method for data analysis and characterization that provides insight into the topics that comprise each document in a corpus, where each topic is described by an associated word distribution. A dynamic topic model is an extension of this model that can be applied to time series data. These models have typically been applied to the text domain where the concepts of tokens and words are well defined. Applying these models to the image domain is non-obvious because the concepts of tokens and words need to hand-crafted. In this work, we apply the dynamic topic model to a sequence of images to provide insight into their dynamic nature, e.g., by helping to identify interesting locations in time that correspond to change in operating conditions We apply this model to images from the KITTI dataset and show that the model captures the evolving nature of these topics over time.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Amit Bhatia and Neil Bomberger "Image sequence analysis using dynamic topic model", Proc. SPIE 13057, Signal Processing, Sensor/Information Fusion, and Target Recognition XXXIII, 130570R (7 June 2024); https://doi.org/10.1117/12.3016281

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