Fine-Grained Visual Classification of Plant Species In The Wild: Object Detection as A Reinforced Means of Attention

• URL: http://arxiv.org/abs/2106.02141v1
• Date: Thu, 3 Jun 2021 21:22:18 GMT
• Title: Fine-Grained Visual Classification of Plant Species In The Wild: Object Detection as A Reinforced Means of Attention
• Authors: Matthew R. Keaton, Ram J. Zaveri, Meghana Kovur, Cole Henderson, Donald A. Adjeroh, Gianfranco Doretto
• Abstract summary: We explore the idea of using object detection as a form of attention to mitigate the effects of data variability. We introduce a bottom-up approach based on detecting plant organs and fusing the predictions of a variable number of organ-based species classifiers. We curate a new dataset with a long-tail distribution for evaluating plant organ detection and organ-based species identification.
• Score: 9.427845067849177
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Plant species identification in the wild is a difficult problem in part due to the high variability of the input data, but also because of complications induced by the long-tail effects of the datasets distribution. Inspired by the most recent fine-grained visual classification approaches which are based on attention to mitigate the effects of data variability, we explore the idea of using object detection as a form of attention. We introduce a bottom-up approach based on detecting plant organs and fusing the predictions of a variable number of organ-based species classifiers. We also curate a new dataset with a long-tail distribution for evaluating plant organ detection and organ-based species identification, which is publicly available.

Related papers

• High-Throughput Phenotyping using Computer Vision and Machine Learning [0.0]
  We used a dataset provided by Oak Ridge National Laboratory with 1,672 images of Populus Trichocarpa with white labels displaying treatment. Optical character recognition (OCR) was used to read these labels on the plants. Machine learning models were used to predict treatment based on those classifications, and analyzed encoded EXIF tags were used for the purpose of finding leaf size and correlations between phenotypes.
  arXiv  Detail & Related papers  (2024-07-08T19:46:31Z)
• All Thresholds Barred: Direct Estimation of Call Density in Bioacoustic Data [1.7916003204531015]
  We propose a validation scheme for estimating call density in a body of data. We use these distributions to predict site-level densities, which may be subject to distribution shifts.
  arXiv  Detail & Related papers  (2024-02-23T14:52:44Z)
• Multi-task Explainable Skin Lesion Classification [54.76511683427566]
  We propose a few-shot-based approach for skin lesions that generalizes well with few labelled data. The proposed approach comprises a fusion of a segmentation network that acts as an attention module and classification network.
  arXiv  Detail & Related papers  (2023-10-11T05:49:47Z)
• Bioinspired random projections for robust, sparse classification [0.0]
  Inspired by the use of random projections in biological sensing systems, we present a new algorithm for processing data in classification problems. This is based on observations of the human brain and the fruit fly's olfactory system and involves randomly projecting data into a space of greatly increased dimension before applying a cap operation to truncate the smaller entries.
  arXiv  Detail & Related papers  (2022-06-18T15:24:20Z)
• Persistent Animal Identification Leveraging Non-Visual Markers [71.14999745312626]
  We aim to locate and provide a unique identifier for each mouse in a cluttered home-cage environment through time. This is a very challenging problem due to (i) the lack of distinguishing visual features for each mouse, and (ii) the close confines of the scene with constant occlusion. Our approach achieves 77% accuracy on this animal identification problem, and is able to reject spurious detections when the animals are hidden.
  arXiv  Detail & Related papers  (2021-12-13T17:11:32Z)
• SLA$^2$P: Self-supervised Anomaly Detection with Adversarial Perturbation [77.71161225100927]
  Anomaly detection is a fundamental yet challenging problem in machine learning. We propose a novel and powerful framework, dubbed as SLA$2$P, for unsupervised anomaly detection.
  arXiv  Detail & Related papers  (2021-11-25T03:53:43Z)
• Geo-Spatiotemporal Features and Shape-Based Prior Knowledge for Fine-grained Imbalanced Data Classification [63.916371837696396]
  Fine-grained classification aims at distinguishing between items with similar global perception and patterns, but that differ by minute details. Our primary challenges come from both small inter-class variations and large intra-class variations. We propose to combine several innovations to improve fine-grained classification within the use-case of wildlife.
  arXiv  Detail & Related papers  (2021-03-21T02:01:38Z)
• Mycorrhiza: Genotype Assignment usingPhylogenetic Networks [2.286041284499166]
  We introduce Mycorrhiza, a machine learning approach for the genotype assignment problem. Our algorithm makes use of phylogenetic networks to engineer features that encode the evolutionary relationships among samples. Mycorrhiza yields particularly significant gains on datasets with a large average fixation index (FST) or deviation from the Hardy-Weinberg equilibrium.
  arXiv  Detail & Related papers  (2020-10-14T02:36:27Z)
• Two-View Fine-grained Classification of Plant Species [66.75915278733197]
  We propose a novel method based on a two-view leaf image representation and a hierarchical classification strategy for fine-grained recognition of plant species. A deep metric based on Siamese convolutional neural networks is used to reduce the dependence on a large number of training samples and make the method scalable to new plant species.
  arXiv  Detail & Related papers  (2020-05-18T21:57:47Z)
• Automatic image-based identification and biomass estimation of invertebrates [70.08255822611812]
  Time-consuming sorting and identification of taxa pose strong limitations on how many insect samples can be processed. We propose to replace the standard manual approach of human expert-based sorting and identification with an automatic image-based technology. We use state-of-the-art Resnet-50 and InceptionV3 CNNs for the classification task.
  arXiv  Detail & Related papers  (2020-02-05T21:38:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.