4.7 Article

Large Margin Multi-Modal Multi-Task Feature Extraction for Image Classification

Journal

IEEE TRANSACTIONS ON IMAGE PROCESSING
Volume 25, Issue 1, Pages 414-427

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2015.2495116

Keywords

Feature extraction; image classification; multi-task; multi-modal; large margin

Funding

  1. Microsoft Research Asia
  2. Singapore Ministry of Education under Grant Tier 1 [RG17/14]
  3. National Key Technology RD Program [2015BAF15B00]
  4. National Natural Science Foundation of China [61375026, 61572463]
  5. Australian Research Council [DP-140102164, FT-130101457]

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The features used in many image analysis-based applications are frequently of very high dimension. Feature extraction offers several advantages in high-dimensional cases, and many recent studies have used multi-task feature extraction approaches, which often outperform single-task feature extraction approaches. However, most of these methods are limited in that they only consider data represented by a single type of feature, even though features usually represent images from multiple modalities. We, therefore, propose a novel large margin multi-modal multi-task feature extraction (LM3FE) framework for handling multi-modal features for image classification. In particular, LM3FE simultaneously learns the feature extraction matrix for each modality and the modality combination coefficients. In this way, LM3FE not only handles correlated and noisy features, but also utilizes the complementarity of different modalities to further help reduce feature redundancy in each modality. The large margin principle employed also helps to extract strongly predictive features, so that they are more suitable for prediction (e.g., classification). An alternating algorithm is developed for problem optimization, and each subproblem can be efficiently solved. Experiments on two challenging real-world image data sets demonstrate the effectiveness and superiority of the proposed method.

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