# Enhancing temporal segmentation by nonlocal self-similarity

## TL;DR

This paper introduces a novel method for improving temporal segmentation of photo-streams by leveraging nonlocal self-similarity, resulting in more accurate event detection in egocentric videos.

## Contribution

It proposes a new approach that encodes long-range temporal dependencies using nonlocal self-similarity, enhancing existing CNN-based features for better segmentation.

## Key findings

- Achieved an average F-measure increase of 3.71% over state-of-the-art methods.
- Demonstrated consistent improvements across seven different CNN features.
- Validated on the EDUB-Seg dataset for egocentric photostream segmentation.

## Abstract

Temporal segmentation of untrimmed videos and photo-streams is currently an active area of research in computer vision and image processing. This paper proposes a new approach to improve the temporal segmentation of photo-streams. The method consists in enhancing image representations by encoding long-range temporal dependencies. Our key contribution is to take advantage of the temporal stationarity assumption of photostreams for modeling each frame by its nonlocal self-similarity function. The proposed approach is put to test on the EDUB-Seg dataset, a standard benchmark for egocentric photostream temporal segmentation. Starting from seven different (CNN based) image features, the method yields consistent improvements in event segmentation quality, leading to an average increase of F-measure of 3.71% with respect to the state of the art.