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Weakly Supervised Random Forest for Multi-Label Image Clustering and Segmentation

Published: 22 June 2015 Publication History

Abstract

Clustering is a useful statistical tool in data mining and computer vision. Supervised information is introduced to improve the clustering performance. However, labeling each piece of data accurately is extremely expensive when the amount of data is huge. Existing supervised clustering methods handle the huge workload of labeling large amount of data by transferring the bag-level labels into the instance-level descriptors. However, each bag has only one label limits the application scope seriously. In this paper, we propose weakly supervised multi-label clustering, which allows to label a bag of data multiple labels. The key technique is a weakly supervised random forest which can calculate the model parameters with a deterministic annealing strategy to optimize the non-convex objective function. The proposed algorithm is applied to two typical applications, image clustering and segmentation problems. Impressive efficiency in both training and testing stages on the state-of-the-art image data sets is achieved in our experiments.

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Cited By

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  • (2023)GA-based weighted ensemble learning for multi-label aerial image classification using convolutional neural networks and vision transformersMachine Learning: Science and Technology10.1088/2632-2153/ad10cf4:4(045045)Online publication date: 7-Dec-2023
  • (2022)Multiple Instance Learning With Random Forest for Event Logs Analysis and Predictive Maintenance in Ship Electric Propulsion SystemIEEE Transactions on Industrial Informatics10.1109/TII.2022.314417718:11(7718-7728)Online publication date: Nov-2022

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  1. Weakly Supervised Random Forest for Multi-Label Image Clustering and Segmentation

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      cover image ACM Conferences
      ICMR '15: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval
      June 2015
      700 pages
      ISBN:9781450332743
      DOI:10.1145/2671188
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Publication History

      Published: 22 June 2015

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      Author Tags

      1. annealing
      2. clustering
      3. multi-label
      4. semantic
      5. weakly supervised

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      • Research-article

      Funding Sources

      • National Natural Science Foundation of China
      • Zhejiang Provincial Natural Science Foundation of China

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      ICMR '15
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      ICMR '15 Paper Acceptance Rate 48 of 127 submissions, 38%;
      Overall Acceptance Rate 254 of 830 submissions, 31%

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      View all
      • (2023)GA-based weighted ensemble learning for multi-label aerial image classification using convolutional neural networks and vision transformersMachine Learning: Science and Technology10.1088/2632-2153/ad10cf4:4(045045)Online publication date: 7-Dec-2023
      • (2022)Multiple Instance Learning With Random Forest for Event Logs Analysis and Predictive Maintenance in Ship Electric Propulsion SystemIEEE Transactions on Industrial Informatics10.1109/TII.2022.314417718:11(7718-7728)Online publication date: Nov-2022

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