A Visual Vocabulary for Flower Classification

By Maria-Elena Nilsback, Andrew Zisserman. 2006

993 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Molecular Biology, Plant Science

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Citations in the corpus

The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition (2015, background): “The majority of research in fine-grained recognition has focused on developing improved models for classification [1,3,5,7,9,8,14,16,18,20,21,22,28,29,36,37,41,42,49,51,50,66,68,69,71,73,72,76,77,75,78].”
Recognition in Terra Incognita (2018, background): “Natural world datasets such as the iNaturalist dataset [1], CUB200 [32], Oxford Flowers [33], LeafSnap [34], and NABirds700 [35] are focused on finegrained species classification and detection.”
Lifelong GAN: Continual Learning for Conditional Image Generation (2019, background): “We also demonstrate Lifelong GAN on a more challenging dataset, which contains higher resolution images from five categories of the Flower dataset [25].”
The Devil is in the Tails: Fine-grained Classification in the Wild (2017, background): “Fine-Grained Visual Classification – The vision community has released many fine-grained datasets covering several domains such as birds [5, 50, 52, 56], dogs [23, 34], airplanes [36, 51], flowers [37], leaves [28], trees [55] and cars[24, 32].”
Bird Species Categorization Using Pose Normalized Deep Convolutional Nets (2014, methodology): “Our architecture first computes an estimate of the object’s pose; this is used to compute local image features which are, in turn, used for classification.”
The iNaturalist Challenge 2017 Dataset (2017, background): “The vision community has released many fine-grained datasets covering several domains such as birds [42, 40, 2, 38, 16], dogs [14, 28, 22], airplanes [23, 39], flowers [26], leaves [18], trees [41] and cars [17, 20, 46, 6].”
Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition (2019, methodology): “To give a visual illustration, we show examples of generated MNIST [46] and larger resolution flower images [50] together with their outlier percentage in figure 3.”
Visual classification with multi-task joint sparse representation (2010, methodology): “2) Oxford Flowers Data Sets: In this subsection, we present results on two Oxford flower data sets with 17 classes [56] and 102 classes [26] respectively.”
Multiple Incomplete Views Clustering via Weighted Nonnegative Matrix Factorization with L2, 1 Regularization (2015, background): “– Oxford Flowers Data (Flowers): The Oxford Flower Dataset is composed of 17 flower categories, with 80 images for each category [30].”
Learning Rich Part Hierarchies With Progressive Attention Networks for Fine-Grained Image Recognition (2020, background): “as bird species [6], [7], flower types [8], and car models [9], [10].”
Part-Based R-CNNs for Fine-Grained Category Detection (2014, background): “Recently, a large body of computer vision research has focused on the fine-grained classificationproblem in a number of domains, such as animal breeds or species [16, 25, 30, 32, 36, 45], plant species [1, 2, 5, 33, 34, 38], and man-made objects [31, 39].”
Deep multi-view learning methods: A review (2021, methodology): “Here are some popular image datasets used for evaluating multi-view learning methods: Caltech 101/256 [199], NUS-WIDE [200], COIL20/100 1, 17flowers [201], Soccer 2, Scene-15 [202], and 640 ORL 3datasets.”
Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis (2021, methodology): “On 1024 × 1024 resolution, we test on Flickr-Face-HQ (FFHQ) (Karras et al., 2019), Oxford-ﬂowers (Nilsback & Zisserman, 2006), art paintings from WikiArt (wikiart.org), photographs on natural landscape from Unsplash (unsplash.com), Pokemon (pokemon.com), anime face, skull, and shell.”
Semantic Autoencoder for Zero-Shot Learning (2017, methodology): “test our algorithm with a real dataset – Oxford Flowers-17 (848 images) [39].”
Fine-Grained Recognition in the Wild: A Multi-task Domain Adaptation Approach (2017, methodology): “To enable these methods, datasets have been proposed to train models recognizing all categories and scenes [15, 35, 54], or focus on the fine-grained recognition task [51, 30, 52, 42, 34].”
ReSeg: A Recurrent Neural Network for Object Segmentation (2015, methodology): “We test the performances of the model in the object segmentation domain on one of the historically most used datasets in this field, the Weizmann Horse dataset (Borenstein, 2004), the Oxford Flowers 17 dataset (Nilsback & Zisserman, 2006) and the more recent Fashionista dataset (Yamaguchi, 2012).”
A codebook-free and annotation-free approach for fine-grained image categorization (2012, background): “There is a number of recent work in fine-grained image classification using various datasets such as flowers [22], stonefly larvae [20], bird species [29], and related leaf nodes of ImageNet [9] such as all fungus [8].”
The Ignorant Led by the Blind: A Hybrid Human–Machine Vision System for Fine-Grained Categorization (2014, background): “Applications include recognizing different species of leaves (Kumar et al. 2012; Belhumeur et al. 2008), flowers (Nilsback and Zisserman 2006, 2008), dogs (Parkhi et al. 2011; Liu et al. 2012; Parkhi et al. 2012; Khosla et al. 2011), birds (Branson et al. 2010; Farrell et al. 2011; Wah et al. 2011;…”
The iNaturalist Species Classification and Detection Dataset (2017, background): “The vision community has released many fine-grained datasets covering several domains such as birds [44, 42, 2, 40, 18], dogs [16, 29, 23], airplanes [24, 41], flowers [27], leaves [20], food [10], trees [43], and cars [19, 22, 48, 7].”