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DeepBag: Recognizing Handbag Models

机译：DeepBag：识别手提包模型

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摘要

In this paper, we address the problem of branded handbag recognition. It is a challenging problem due to the non-rigid deformation, illumination changes, and inter-class similarity. We propose a novel framework based on deep convolutional neural network (CNN). Concretely, we propose a new CNN model, called feature selective joint classification - regression CNN (FSCR-CNN). Its advantages lie in two folds: 1) it alleviates the illumination changes by a feature selection strategy to focus on the color- nondiscriminative features in the network learning, and 2) rather than only targeting on the hard label (i.e., the handbag model), it also incorporates a soft label (i.e., a distribution measuring the similarity between the ground truth model and all the models to be trained) to construct the loss function for training CNN, which leads to a better classifier for handbags with large inter-class similarity. We evaluate the performance of our framework on a newly built branded handbag dataset. The results show that it performs favorably for recognizing handbags with 94.48% in accuracy. We also apply the proposed FSCR-CNN model in recognizing other fine-grained objects with state-of-the-art CNN architectures, which is able to achieve over 5% improvement in accuracy.
机译：在本文中，我们解决了品牌手袋识别的问题。由于非刚性变形，照度变化和类间相似性，这是一个具有挑战性的问题。我们提出了一种基于深度卷积神经网络（CNN）的新颖框架。具体而言，我们提出了一种新的CNN模型，称为特征选择性联合分类-回归CNN（FSCR-CNN）。它的优点有两个方面：1）通过特征选择策略缓解照明变化，以专注于网络学习中的颜色非歧视性特征; 2）而不是仅针对硬标签（即手提包型号） ，它还合并了一个软标签（即一种用于测量地面真实模型与所有待训练模型之间相似度的分布），以构建用于训练CNN的损失函数，从而为具有较大类别间手袋的手袋提供了更好的分类器相似。我们在新建的品牌手袋数据集上评估框架的性能。结果表明，该方法在识别手提包方面具有良好的准确性，准确度为94.48％。我们还将提出的FSCR-CNN模型应用于具有最新CNN架构的其他细粒度对象，从而能够将准确性提高超过5％。

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