Stand-Alone Objective Segmentation Quality Evaluation
Open accessAbstract
The identification of objects in video sequences, that is, video segmentation, plays a major role in emerging interactive multimedia services, such as those enabled by the ISO MPEG- and MPEG- standards. In this context, assessing the adequacy of the identified objects to the application targets, that is, evaluating the segmentation quality, assumes a crucial importance. Video segmentation technology has received considerable attention in the literature, with algorithms being proposed to address various types of applications. However, the segmentation quality performance evaluation of those algorithms is often ad hoc, and a well-established solution is not available. In fact, the field of objective segmentation quality evaluation is still maturing; recently, some more efforts have been made, mainly following the emergence of the MPEG object-based coding and description standards. This paper discusses the problem of objective segmentation quality evaluation in its most difficult scenario: standalone evaluation, that is, when a reference segmentation is not available for comparative evaluation. In particular, objective metrics are proposed for the evaluation of standalone segmentation quality for both individual objects and overall segmentation partitions.
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Existing methods for evaluating segmentation without ground truth cannot be applied to a general task with many different class labels.
“However, these methods cannot be applied to a more general task, such as an image with many different class labels to be segmented.”
From Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth (2017) · cites this paper as [18]
The Introduction and Section 3.2 say that stand-alone evaluation works best in constrained scenarios and that a single general-purpose composite metric cannot be established. They do not identify a large number of class labels as the limitation; Section 3.2 also says metrics can be adapted for content outside its two proposed classes. The categorical claim that these methods cannot be applied to such an image therefore goes beyond the paper. (LLM, conf=0.86)
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Research works printed but not linked: 9
- G. Rees and P. Greenway, "Metrics for image segmentation," in Workshop on Performance Characterisation and Benchmark- ing of Vision Systems, pp. 20-37, Essex, UK, January 1999.
- Y. Zhang and J. Gerbrands, "Objective and quantitative seg- mentation evaluation and comparison," Signal Processing, vol. 39, no. 1-2, pp. 43-54, 1994.
- Y. Zhang, "A survey on evaluation methods for image seg- mentation," Pattern Recognition, vol. 29, no. 8, pp. 1335-1346, 1996.
- M. Heath, S. Sarkar, T. Sanocki, and K. Bowyer, "A robust visual method for assessing the relative performance of edge- detection algorithms," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 19, no. 12, pp. 1338-1359, 1997.
- M. Levine and A. Nazif, "Dynamic measurement of computer generated image segmentations," IEEE Trans. on Pattern Anal- ysis and Machine Intelligence, vol. 7, no. 2, pp. 155-164, 1985.
- P. Villegas, X. Marichal, and A. Salcedo, "Objective evaluation of segmentation masks in video sequences," in WIAMIS' 99, pp. 85-88, Germany, 31 May-1 June 1999.
- P. Correia and F. Pereira, "Objective evaluation of relative segmentation quality," in Int. Conference on Image Processing (ICIP), pp. 308-311, Vancouver, Canada, September 2000.
- J. Serra, Image Analysis and Mathematical Morphology, vol. 1, Academic Press, San Diego, Calif, USA, 1988.
- P. Correia and F. Pereira, "Estimation of video object's rele- vance," in EUSIPCO' 2000, pp. 925-928, Finland, September 2000.
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Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth
Valindria, V. V., Lavdas, I., Bai, W., Kamnitsas, K., Aboagye, E. O., Rockall, A. G., Rueckert, D., & Glocker, B. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2665165 · Cited by 152 · see citing works
- Authors
- Correia, P. L., & Pereira, F.
- Affiliations
- Instituto Superior Técnico (PT)
- Corresponding author
- Correia, Paulo Lobato
- Venue
- EURASIP Journal on Advances in Signal Processing
- Published
- 2002-04-30
- Volume
- 2002
- Issue
- 4
- ISSN
- 1110-8657 (ISSN-L)
- 1687-6180
- OpenAlex
- W1981710537
- Cited by
- 35
- References
- 0
- Citations by year
- 2025: 4 · 2024: 1 · 2023: 1 · 2017: 2 · 2016: 1 · 2015: 2 · 2014: 4 · 2013: 1 · 2012: 1
- FWCI
- 1.13
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- EURASIP Journal on Advances in Signal Processing (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Signal Processing › Video Coding and Compression Technologies
- Keywords
- video segmentation, MPEG-7, segmentation quality metrics, object identification
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