Journal Article10.1109/TSMC.2013.2297398
A Multiple-Feature and Multiple-Kernel Scene Segmentation Algorithm for Humanoid Robot
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TL;DR: A new clustering method, which is called feature validity-interval type- 2 fuzzy C-means (FV-IT2FCM) clustering algorithm, is proposed by integrating a type-2 fuzzy criterion in the clustering optimization process to improve the robustness and reliability of clustering results by the iterative optimization.
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Abstract: This technical correspondence presents a multiple-feature and multiple-kernel support vector machine (MFMK-SVM) methodology to achieve a more reliable and robust segmentation performance for humanoid robot. The pixel wise intensity, gradient, and C1 SMF features are extracted via the local homogeneity model and Gabor filter, which would be used as inputs of MFMK-SVM model. It may provide multiple features of the samples for easier implementation and efficient computation of MFMK-SVM model. A new clustering method, which is called feature validity-interval type-2 fuzzy C-means (FV-IT2FCM) clustering algorithm, is proposed by integrating a type-2 fuzzy criterion in the clustering optimization process to improve the robustness and reliability of clustering results by the iterative optimization. Furthermore, the clustering validity is employed to select the training samples for the learning of the MFMK-SVM model. The MFMK-SVM scene segmentation method is able to fully take advantage of the multiple features of scene image and the ability of multiple kernels. Experiments on the BSDS dataset and real natural scene images demonstrate the superior performance of our proposed method.
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Citations
Fuzzy C-Means clustering algorithm for data with unequal cluster sizes and contaminated with noise and outliers: Review and development
TL;DR: This work revises the FCM algorithm to make it applicable to data with unequal cluster sizes, noise and outliers, and non-uniform mass distribution and shows that the RFCM algorithm works for both cases and outperforms the both categories of the algorithms.
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Hybrid Classifier Ensemble for Imbalanced Data
TL;DR: A hybrid optimal ensemble classifier framework that combines density-based undersampling and cost-effective methods through exploring state-of-the-art solutions using multi-objective optimization algorithm is proposed.
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Cluster Prototypes and Fuzzy Memberships Jointly Leveraged Cross-Domain Maximum Entropy Clustering
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TL;DR: The specific cluster prototypes and fuzzy memberships jointly leveraged CPM-JL-CDMEC features high-clustering effectiveness and robustness even in some complex data situations, and the reliability of FM-CDDM has been demonstrated to be close to well-established external criteria.
Wavelet Frame-Based Fuzzy C -Means Clustering for Segmenting Images on Graphs
TL;DR: Experimental results demonstrate that the proposed FCM algorithm is effective and efficient, and has a better ability for segmentation of images on graphs than other improved FCM algorithms existing in the literature.
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Review of Recent Type-2 Fuzzy Image Processing Applications
TL;DR: It is shown that type-2 fuzzy sets outperform both traditional image processing techniques as well as techniques using type-1 fuzzy sets, and provide the ability to handle uncertainty when the image is corrupted by noise.
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