Object-based image labeling through learning by example and multi-level segmentation

Date

2003-06-01

Author

Xu, Y
Duygulu, P
Saber, E
Tekalp, AM
Yarman Vural, Fatoş Tunay

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

53
views

0
downloads

We propose a method for automatic extraction and labeling of semantically meaningful image objects using "learning by example" and threshold-free multi-level image segmentation. The proposed method scans through images, each of which is pre-segmented into a hierarchical uniformity tree, to seek and label objects that are similar to an example object presented by the user. By representing images with stacks of multi-level segmentation maps, objects can be extracted in the segmentation map level with adequate detail. Experiments have shown that the proposed multi-level image segmentation results in significant reduction in computation complexity for object extraction and labeling (compared to a single fine-level segmentation) by avoiding unnecessary tests of combinations in finer levels. The multi-level segmentation-based approach also achieves better accuracy in detection and labeling of small objects. (C) 2003 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

Subject Keywords

Signal Processing, Software, Artificial Intelligence, Computer Vision and Pattern Recognition

URI

https://hdl.handle.net/11511/62981

Journal

PATTERN RECOGNITION

DOI

https://doi.org/10.1016/s0031-3203(02)00250-9

Collections

Department of Computer Engineering, Article

Suggestions

OpenMETU
Core

Key protected classification for collaborative learning Sariyildiz, Mert Bulent; Cinbiş, Ramazan Gökberk; Ayday, Erman (Elsevier BV, 2020-08-01) © 2020Large-scale datasets play a fundamental role in training deep learning models. However, dataset collection is difficult in domains that involve sensitive information. Collaborative learning techniques provide a privacy-preserving solution, by enabling training over a number of private datasets that are not shared by their owners. However, recently, it has been shown that the existing collaborative learning frameworks are vulnerable to an active adversary that runs a generative adversarial network (GAN...
Multiple description coding of animated meshes Bici, M. Oguz; Akar, Gözde (Elsevier BV, 2010-11-01) In this paper, we propose three novel multiple description coding (MDC) methods for reliable transmission of compressed animated meshes represented by series of 3D static meshes with same connectivity. The proposed methods trade off reconstruction quality for error resilience to provide the best expected reconstruction of 3D mesh sequence at the decoder side. The methods are based on layer duplication and partitioning of the set of vertices of a scalable coded animated mesh by either spatial or temporal sub...
One-dimensional representation of two-dimensional information for HMM based handwriting recognition Arica, N; Yarman Vural, Fatoş Tunay (Elsevier BV, 2000-06-01) In this study, we introduce a one-dimensional feature set, which embeds two-dimensional information into an observation sequence of one-dimensional string, selected from a code-book. It provides a consistent normalization among distinct classes of shapes, which is very convenient for Hidden Markov Model (HMM) based shape recognition schemes. The normalization parameters, which maximize the recognition rate, are dynamically estimated in the training stage of HMM. The proposed recognition system is tested on ...
Low-level multiscale image segmentation and a benchmark for its evaluation Akbaş, Emre (Elsevier BV, 2020-10-01) In this paper, we present a segmentation algorithm to detect low-level structure present in images. The algorithm is designed to partition a given image into regions, corresponding to image structures, regardless of their shapes, sizes, and levels of interior homogeneity. We model a region as a connected set of pixels that is surrounded by ramp edge discontinuities where the magnitude of these discontinuities is large compared to the variation inside the region. Each region is associated with a scale that d...
Camera auto-calibration using a sequence of 2D images with small rotations Hassanpour, R; Atalay, Mehmet Volkan (Elsevier BV, 2004-07-02) In this study, we describe an auto-calibration algorithm with fixed but unknown camera parameters. We have modified Triggs' algorithm to incorporate known aspect ratio and skew values to make it applicable for small rotation around a single axis. The algorithm despite being a quadratic one is easy to solve. We have applied the algorithm to some artificial objects with known size and dimensions for evaluation purposes. In addition, the accuracy of the algorithm has been verified using synthetic data. The des...

Citation Formats

Y. Xu, P. Duygulu, E. Saber, A. Tekalp, and F. T. Yarman Vural, “Object-based image labeling through learning by example and multi-level segmentation,” PATTERN RECOGNITION, pp. 1407–1423, 2003, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/62981.