Invariant Image-Based Species Classification of Butterflies and Reef Fish
Invariant Image-Based Species Classification of Butterflies and Reef Fish
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基于不变图像的蝴蝶和礁鱼物种分类
DOI：
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发表时间：
2015
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通讯作者：
M. Kampel
中科院分区：
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作者：
Hafeez Anwar;S. Zambanini;M. Kampel
提出了一种基于物种的蝴蝶和珊瑚鱼图像分类框架。为了支持这种基于图像的分类，我们使用了一种图像表示，该表示用空间信息丰富了著名的视觉词袋（BoVWs）模型。这种图像表示是通过以比例和旋转不变的方式编码二维图像平面上视觉单词的全局几何关系来开发的。通过这种方式，这些动物的图像中最常见的变化实现了不变性，因为它们可以在不同的图像位置成像，在图像中表现出不同的平面内方向和不同的比例。我们的蝴蝶和珊瑚鱼数据集中的图像属于每种动物的30个物种。我们在两个数据集上都获得了比普通BoVWs模型更好的分类率，同时仍然对上述图像变化保持不变。我们提出的基于图像的蝴蝶和珊瑚礁鱼类分类框架可以被认为是科学研究，对话和教育的有用工具。
We propose a framework for species-based image classification of butterflies and reef fish. To support such image-based classification, we use an image representation which enriches the famous bag-of-visual words (BoVWs) model with spatial information. This image representation is developed by encoding the global geometric relationships of visual words in the 2D image plane in a scaleand rotation-invariant manner. In this way, invariance is achieved to the most common variations found in the images of these animals as they can be imaged at different image locations, exhibit various in-plane orientations and have various scales in the images. The images in our butterfly and reef fish datasets belong to 30 species of each animal. We achieve better classification rates on both the datasets than the ordinary BoVWs model while still being invariant to the mentioned image variations. Our proposed image-based classification framework for butterfly and reef fish species can be considered as a helpful tool for scientific research, conversation and education.