Image segmentation algorithms based on the machine learning of features

Authors:
Sang Hak Lee;Hyung Il Koo;Nam Ik Cho
Affiliations:
School of Electrical Engineering, Seoul National University, San56-1, Shilim-Dong, Kwanak-Gu, Seoul, Republic of Korea;School of Electrical Engineering, Seoul National University, San56-1, Shilim-Dong, Kwanak-Gu, Seoul, Republic of Korea;School of Electrical Engineering, Seoul National University, San56-1, Shilim-Dong, Kwanak-Gu, Seoul, Republic of Korea
Venue:
Pattern Recognition Letters
Year:
2010

Citing 24
Cited 2

Filters, Random Fields and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling

International Journal of Computer Vision
Segmentation of page images using the area Voronoi diagram

Computer Vision and Image Understanding - Special issue on document image understanding and retrieval
Normalized Cuts and Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Segmentation of Color-Texture Regions in Images and Video

IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Approximate Energy Minimization via Graph Cuts

IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Segmentation by Data-Driven Markov Chain Monte Carlo

IEEE Transactions on Pattern Analysis and Machine Intelligence
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying

IEEE Transactions on Pattern Analysis and Machine Intelligence
Classifying Images of Materials: Achieving Viewpoint and Illumination Independence

ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data

ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Boosting the margin: A new explanation for the effectiveness of voting methods

ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Synergism in Low Level Vision

ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
"GrabCut": interactive foreground extraction using iterated graph cuts

ACM SIGGRAPH 2004 Papers
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision

IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminative Random Fields

International Journal of Computer Vision
Pixel-Accurate Representation and Evaluation of Page Segmentation in Document Images

ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Toward Objective Evaluation of Image Segmentation Algorithms

IEEE Transactions on Pattern Analysis and Machine Intelligence
TEXEMS: Texture Exemplars for Defect Detection on Random Textured Surfaces

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors

IEEE Transactions on Pattern Analysis and Machine Intelligence
Color-texture segmentation using unsupervised graph cuts

Pattern Recognition
Support vector random fields for spatial classification

PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Integrated active contours for texture segmentation

IEEE Transactions on Image Processing
Interactive Image Segmentation via Adaptive Weighted Distances

IEEE Transactions on Image Processing
Text Extraction and Document Image Segmentation Using Matched Wavelets and MRF Model

IEEE Transactions on Image Processing

Scalable image quality assessment with 2D mel-cepstrum and machine learning approach

Pattern Recognition
Multi-scale image segmentation algorithm based on support vector machine approximation criteria

Concurrency and Computation: Practice & Experience

Quantified Score

Hi-index	0.10

Visualization

Abstract

For general purpose image segmentation, it is required to find and integrate the features that best characterize the regions to be segmented. This paper proposes a machine learning approach to finding the appropriate features and also a new segmentation method based on the information obtained while learning. Precisely, our method is based on the AdaBoost algorithm for learning the difference between regions, and the CRF-based (conditional random fields) energy formulation for the segmentation using the information from the learning. We have applied our method to interactive (semi-automatic) and unsupervised (fully-automatic) segmentation problems. While the interactive case is relatively straightforward due to the nature of our machine learning scheme, the unsupervised case is not. Hence, for the unsupervised segmentation, we devise a new initialization method and an EM-like (Expectation-Maximization) optimization method that iterates AdaBoost learning and graph-cuts. The analysis shows that the number of regions is automatically determined so that only distinguishable regions are survived. Experimental results also show that the proposed method gives promising results in diverse applications such as texture segmentation, color-texture segmentation, and page segmentation.