Abstract This paper investigates the issue of real-world identification to fulfill better species protection. We focus on plant species identification as it is a classic and hot issue. In tradition plant species identification the samples are scanned specimen and the background is simple. However, real-world species recognition is more challenging. We first systematically investigate what is realistic species recognition and the difference from tradition plant species recognition. To deal with the challenging task, an interdisciplinary collaboration is presented based on the latest advances in computer science and technology. We propose a novel framework and an effective data augmentation method for deep learning in this paper. We first crop the image in terms of visual attention before general recognition. Besides, we apply it as a data augmentation method. We call the novel data augmentation approach attention cropping (AC). Deep convolutional neural networks are trained to predict species from a large amount of data. Extensive experiments on traditional dataset and specific dataset for real-world recognition are conducted to evaluate the performance of our approach. Experiments first demonstrate that our approach achieves state-of-the-art results on different types of datasets. Besides, we also evaluate the performance of data augmentation method AC. Results show that AC provides superior performance. Compared with the precision of methods without AC, the results with AC achieve substantial improvement.
Afterwards, flower image samples begin to be employed. Oxford flower is applied in many related researches @cite_11 @cite_18 @cite_16 @cite_27 . @cite_11 developped a visual vocabulary that explicitly represents the various aspects(colour, shape, and texture) that distinguish one flower from another. @cite_16 investigated to what extent combinations of features can improve classification performance on a large dataset of flower classes. Samples of Oxford flower dataset are shown in the last row of Figure . The work of @cite_18 and @cite_27 is similar. They focused on algorithms for automatically segmenting flowers in colour photographs.
@cite_27 @cite_18 @cite_16 @cite_11
2150856297 2114705035 2039875163 2770776392
We investigate to what extent bag of visual words models can be used to distinguish categories which have significant visual similarity. To this end we develop and optimize a nearest neighbour classifier architecture, which is evaluated on a very challenging database of flower images. The flower categories are chosen to be indistinguishable on colour alone (for example), and have considerable variation in shape, scale, and viewpoint. We demonstrate that by developing a visual vocabulary that explicitly represents the various aspects (colour, shape, and texture) that distinguish one flower from another, we can overcome the ambiguities that exist between flower categories. The novelty lies in the vocabulary used for each aspect, and how these vocabularies are combined into a final classifier. The various stages of the classifier (vocabulary selection and combination) are each optimized on a validation set. Results are presented on a dataset of 1360 images consisting of 17 flower species. It is shown that excellent performance can be achieved, far surpassing standard baseline algorithms using (for example) colour cues alone. We describe an algorithm for automatically segmenting flowers in colour photographs. This is a challenging problem because of the sheer variety of flower classes, the intra-class variability, the variation within a particular flower, and the variability of imaging conditions ‐ lighting, pose, foreshortening etc. The method couples two models ‐ a colour model for foreground and background, and a generic shape model for the pet al structure. This shape model is tolerant to viewpoint changes and pet al deformations, and applicable across many different flower classes. The segmentations are produced using a MRF cost function optimized using graph cuts. The algorithm is tested on 13 flower classes and more than 750 examples. Performance is assessed against ground truth segmentations. We describe an algorithm for automatically segmenting flowers in colour photographs. This is a challenging problem because of the sheer variety of flower classes, the variability within a class and within a particular flower, and the variability of the imaging conditions - lighting, pose, foreshortening, etc. The method couples two models - a colour model for foreground and background, and a light generic shape model for the pet al structure. This shape model is tolerant to viewpoint changes and pet al deformations, and applicable across many different flower classes. The segmentations are produced using a MRF cost function optimized using graph cuts. We show how the components of the algorithm can be tuned to overcome common segmentation errors, and how performance can be optimized by learning parameters on a training set. The algorithm is evaluated on 13 flower classes and more than 750 examples. Performance is assessed against ground truth trimap segmentations. The algorithms is also compared to several previous approaches for flower segmentation. We investigate the problem of Language-Based Image Editing (LBIE) in this work. Given a source image and a natural language description, we want to generate a target image by editing the source im- age based on the description. We propose a generic modeling framework for two sub-tasks of LBIE: language-based image segmentation and image colorization. The framework uses recurrent attentive models to fuse image and language features. Instead of using a fixed step size, we introduce for each re- gion of the image a termination gate to dynamically determine in each inference step whether to continue extrapolating additional information from the textual description. The effectiveness of the framework has been validated on three datasets. First, we introduce a synthetic dataset, called CoSaL, to evaluate the end-to-end performance of our LBIE system. Second, we show that the framework leads to state-of-the- art performance on image segmentation on the ReferIt dataset. Third, we present the first language-based colorization result on the Oxford-102 Flowers dataset, laying the foundation for future research.