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Semantic region of interest and species classification in the deep neural network feature domain
- Ahmed, Ahmed, Yousif, Hayder, Kays, Roland, He, Zhihai
- Ecological informatics 2019 v.52 pp. 57-68
- animals, cameras, data collection, models
- In this paper, we focus on animal object detection and species classification in camera-trap images collected in highly cluttered natural scenes. Using a deep neural network (DNN) model training for animal- background image classification, we analyze the input camera-trap images to generate a multi-level visual representation of the input image. We detect semantic regions of interest for animals from this representation using k-mean clustering and graph cut in the DNN feature domain. These animal regions are then classified into animal species using multi-class deep neural network model. According the experimental results, our method achieves 99.75% accuracy for classifying animals and background and 90.89% accuracy for classifying 26 animal species on the Snapshot Serengeti dataset, outperforming existing image classification methods.