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Image-based fish recognition

机译：基于图像的鱼识别

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We are studying image-based fish identification. Most of traditional approaches used a fish image which was easy to extract a fish region with a white background or uniform background for automatic processing. This research adapted an approach to give several points by manual operation by the user. The proposed approach is able to accept the fish image in the complicated background taken on the rocky place. Furthermore, to investigate the efficient features for fish recognition, we defined various features, such as, shape features, local features, and six kinds of texture features. We collected 129 species under various photography conditions, and the proposed method was carried out to it. As the results, it was confirmed that a combination features with geometric features and BoVW models obtained the highest recognition accuracy.
机译：我们正在研究基于图像的鱼类识别。大多数传统方法使用了一种易于提取鱼区的鱼类图像，其中具有白色背景或均匀的自动处理。这项研究改编了一种方法，通过用户手动操作给出几个点。所提出的方法能够接受在岩石地上的复杂背景中的鱼形象。此外，为了调查鱼识别的有效功能，我们定义了各种功能，例如形状特征，局部功能和六种纹理功能。我们在各种摄影条件下收集了129种物种，并将其进行了拟议的方法。结果，证实了具有几何特征和BOVW模型的组合特征获得了最高识别精度。

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