MultiSegVA: Using Visual Analytics to Segment Biologging Time Series on Multiple Scales

• URL: http://arxiv.org/abs/2009.00548v2
• Date: Wed, 2 Sep 2020 08:22:29 GMT
• Title: MultiSegVA: Using Visual Analytics to Segment Biologging Time Series on Multiple Scales
• Authors: Philipp Meschenmoser, Juri F. Buchm\"uller, Daniel Seebacher, Martin Wikelski and Daniel A. Keim
• Abstract summary: We present our MultiSegVA platform for interactively defining segmentation techniques and parameters on multiple temporal scales. To flexibly compose the multi-scale segmentation, the platform contributes a new visual query language. We demonstrate the applicability and usefulness of MultiSegVA in two real-world use cases from movement ecology.
• Score: 10.51336067116119
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Segmenting biologging time series of animals on multiple temporal scales is an essential step that requires complex techniques with careful parameterization and possibly cross-domain expertise. Yet, there is a lack of visual-interactive tools that strongly support such multi-scale segmentation. To close this gap, we present our MultiSegVA platform for interactively defining segmentation techniques and parameters on multiple temporal scales. MultiSegVA primarily contributes tailored, visual-interactive means and visual analytics paradigms for segmenting unlabeled time series on multiple scales. Further, to flexibly compose the multi-scale segmentation, the platform contributes a new visual query language that links a variety of segmentation techniques. To illustrate our approach, we present a domain-oriented set of segmentation techniques derived in collaboration with movement ecologists. We demonstrate the applicability and usefulness of MultiSegVA in two real-world use cases from movement ecology, related to behavior analysis after environment-aware segmentation, and after progressive clustering. Expert feedback from movement ecologists shows the effectiveness of tailored visual-interactive means and visual analytics paradigms at segmenting multi-scale data, enabling them to perform semantically meaningful analyses. A third use case demonstrates that MultiSegVA is generalizable to other domains.

Related papers

• PSVMA+: Exploring Multi-granularity Semantic-visual Adaption for Generalized Zero-shot Learning [116.33775552866476]
  Generalized zero-shot learning (GZSL) endeavors to identify the unseen using knowledge from the seen domain. GZSL suffers from insufficient visual-semantic correspondences due to attribute diversity and instance diversity. We propose a multi-granularity progressive semantic-visual adaption network, where sufficient visual elements can be gathered to remedy the inconsistency.
  arXiv  Detail & Related papers  (2024-10-15T12:49:33Z)
• Frequency-based Matcher for Long-tailed Semantic Segmentation [22.199174076366003]
  We focus on a relatively under-explored task setting, long-tailed semantic segmentation (LTSS) We propose a dual-metric evaluation system and construct the LTSS benchmark to demonstrate the performance of semantic segmentation methods and long-tailed solutions. We also propose a transformer-based algorithm to improve LTSS, frequency-based matcher, which solves the oversuppression problem by one-to-many matching.
  arXiv  Detail & Related papers  (2024-06-06T09:57:56Z)
• Joint Depth Prediction and Semantic Segmentation with Multi-View SAM [59.99496827912684]
  We propose a Multi-View Stereo (MVS) technique for depth prediction that benefits from rich semantic features of the Segment Anything Model (SAM) This enhanced depth prediction, in turn, serves as a prompt to our Transformer-based semantic segmentation decoder.
  arXiv  Detail & Related papers  (2023-10-31T20:15:40Z)
• Improving Anomaly Segmentation with Multi-Granularity Cross-Domain Alignment [17.086123737443714]
  Anomaly segmentation plays a pivotal role in identifying atypical objects in images, crucial for hazard detection in autonomous driving systems. While existing methods demonstrate noteworthy results on synthetic data, they often fail to consider the disparity between synthetic and real-world data domains. We introduce the Multi-Granularity Cross-Domain Alignment framework, tailored to harmonize features across domains at both the scene and individual sample levels.
  arXiv  Detail & Related papers  (2023-08-16T22:54:49Z)
• Multispectral Image Segmentation in Agriculture: A Comprehensive Study on Fusion Approaches [0.9790238684654974]
  This paper concentrates on the use of fusion approaches to enhance the segmentation process in agricultural applications. In this work, we compare different fusion approaches by combining RGB and NDVI as inputs for crop row detection. The experiments reveal that classical segmentation methods, utilizing techniques such as edge detection and thresholding, can effectively compete with DL-based algorithms.
  arXiv  Detail & Related papers  (2023-07-31T21:24:41Z)
• Segment Everything Everywhere All at Once [124.90835636901096]
  We present SEEM, a promptable and interactive model for segmenting everything everywhere all at once in an image. We propose a novel decoding mechanism that enables diverse prompting for all types of segmentation tasks. We conduct a comprehensive empirical study to validate the effectiveness of SEEM across diverse segmentation tasks.
  arXiv  Detail & Related papers  (2023-04-13T17:59:40Z)
• Visual Semantic Segmentation Based on Few/Zero-Shot Learning: An Overview [47.10687511208189]
  This paper focuses on the recently published few/zero-shot visual semantic segmentation methods varying from 2D to 3D space. Three typical instantiations are involved to uncover the interactions of few/zero-shot learning with visual semantic segmentation.
  arXiv  Detail & Related papers  (2022-11-13T13:39:33Z)
• AF$_2$: Adaptive Focus Framework for Aerial Imagery Segmentation [86.44683367028914]
  Aerial imagery segmentation has some unique challenges, the most critical one among which lies in foreground-background imbalance. We propose Adaptive Focus Framework (AF$), which adopts a hierarchical segmentation procedure and focuses on adaptively utilizing multi-scale representations. AF$ has significantly improved the accuracy on three widely used aerial benchmarks, as fast as the mainstream method.
  arXiv  Detail & Related papers  (2022-02-18T10:14:45Z)
• Prototypical Cross-Attention Networks for Multiple Object Tracking and Segmentation [95.74244714914052]
  Multiple object tracking and segmentation requires detecting, tracking, and segmenting objects belonging to a set of given classes. We propose Prototypical Cross-Attention Network (PCAN), capable of leveraging rich-temporal information online. PCAN outperforms current video instance tracking and segmentation competition winners on Youtube-VIS and BDD100K datasets.
  arXiv  Detail & Related papers  (2021-06-22T17:57:24Z)
• Target-Aware Object Discovery and Association for Unsupervised Video Multi-Object Segmentation [79.6596425920849]
  This paper addresses the task of unsupervised video multi-object segmentation. We introduce a novel approach for more accurate and efficient unseen-temporal segmentation. We evaluate the proposed approach on DAVIS$_17$ and YouTube-VIS, and the results demonstrate that it outperforms state-of-the-art methods both in segmentation accuracy and inference speed.
  arXiv  Detail & Related papers  (2021-04-10T14:39:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.