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.
There are amounts of plant identification approaches that use digital images. As stated above, early algorithms are mainly with leaves. Flavia @cite_2 and Swedish leaf database @cite_26 are two typical leaf datasets. Samples of Flavia and Swedish leaf are shown in Figure . @cite_7 employed Probabilistic Neural Network(PNN) with image and data processing techniques to implement a general purpose automated leaf recognition for plant classification. Using Artificial Neural Network(ANN), @cite_13 designed a computer vision classifier to identify the different Swedish tree classes in terms of their leaves. Many methods employed shape or curvature features as plants are basically classified according to the shapes of their leaves @cite_9 @cite_17 @cite_0 @cite_31 @cite_15 . Shape or curvature features are relatively discriminative for leaf images according to the theory of plant shape taxonomy. It is efficient especially when image contents are extreme simple. Two-dimensional multifractal detrended fluctuation analysis is used for plant classification in @cite_10 .
@cite_26 @cite_7 @cite_15 @cite_10 @cite_9 @cite_0 @cite_2 @cite_31 @cite_13 @cite_17
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In this paper, we employ probabilistic neural network (PNN) with image and data processing techniques to implement a general purpose automated leaf recognition for plant classification. 12 leaf features are extracted and orthogonalized into 5 principal variables which consist the input vector of the PNN. The PNN is trained by 1800 leaves to classify 32 kinds of plants with an accuracy greater than 90 . Compared with other approaches, our algorithm is an accurate artificial intelligence approach which is fast in execution and easy in implementation. Part structure and articulation are of fundamental importance in computer and human vision. We propose using the inner-distance to build shape descriptors that are robust to articulation and capture part structure. The inner-distance is defined as the length of the shortest path between landmark points within the shape silhouette. We show that it is articulation insensitive and more effective at capturing part structures than the Euclidean distance. This suggests that the inner-distance can be used as a replacement for the Euclidean distance to build more accurate descriptors for complex shapes, especially for those with articulated parts. In addition, texture information along the shortest path can be used to further improve shape classification. With this idea, we propose three approaches to using the inner-distance. The first method combines the inner-distance and multidimensional scaling (MDS) to build articulation invariant signatures for articulated shapes. The second method uses the inner-distance to build a new shape descriptor based on shape contexts. The third one extends the second one by considering the texture information along shortest paths. The proposed approaches have been tested on a variety of shape databases, including an articulated shape data set, MPEG7 CE-Shape-1, Kimia silhouettes, the ETH-80 data set, two leaf data sets, and a human motion silhouette data set. In all the experiments, our methods demonstrate effective performance compared with other algorithms Background In this paper, a novel method is proposed to identify plant species by using the two- dimensional multifractal detrended fluctuation analysis (2D MF-DFA). Our method involves calculating a set of multifractal parameters that characterize the texture features of each plant leaf image. An index, I0, that characterizes the relation of the intra-species variances and inter-species variances is introduced. This index is used to select three multifractal parameters for the identification process. The procedure is applied to the Swedish leaf data set containing leaves from fifteen different tree species. Shape similarity and shape retrieval are very important topics in computer vision. The recent progress in this domain has been mostly driven by designing smart shape descriptors for providing better similarity measure between pairs of shapes. In this paper, we provide a new perspective to this problem by considering the existing shapes as a group, and study their similarity measures to the query shape in a graph structure. Our method is general and can be built on top of any existing shape similarity measure. For a given similarity measure, a new similarity is learned through graph transduction. The new similarity is learned iteratively so that the neighbors of a given shape influence its final similarity to the query. The basic idea here is related to PageRank ranking, which forms a foundation of Google Web search. The presented experimental results demonstrate that the proposed approach yields significant improvements over the state-of-art shape matching algorithms. We obtained a retrieval rate of 91.61 percent on the MPEG-7 data set, which is the highest ever reported in the literature. Moreover, the learned similarity by the proposed method also achieves promising improvements on both shape classification and shape clustering. In this paper, an efficient computer-aided plant species identification (CAPSI) approach is proposed, which is based on plant leaf images using a shape matching technique. Firstly, a Douglas - Peucker approximation algorithm is adopted to the original leaf shapes and a new shape representation is used to form the sequence of invariant attributes. Then a modified dynamic programming (MDP) algorithm for shape matching is proposed for the plant leaf recognition. Finally, the superiority of our proposed method over traditional approaches to plant species identification is demonstrated by experiment. The experimental result showed that our proposed algorithm for leaf shape matching is very suitable for the recognition of not only intact but also partial, distorted and overlapped plant leaves due to its robustness. This paper presents a novel approach for both fast and accurately retrieving similar shapes. A hierarchical string cuts (HSCs) method is proposed to partition a shape into multiple level curve segments of different lengths from a point moving around the contour to describe the shape gradually and completely from the global information to the finest details. At each hierarchical level, the curve segments are cut by strings to extract features that characterize the geometric and distribution properties in that particular level of details. The translation, rotation, scale, and mirror invariant HSC descriptor enables a fast metric-based matching to achieve the desired high accuracy. Encouraging experimental results on four databases demonstrated that the proposed method can consistently achieve higher (or similar) retrieval accuracies than the state-of-the-art benchmarks with a more than 120 times faster speed. This may suggest a new way of developing shape retrieval techniques in which a high accuracy can be achieved by a fast metric matching algorithm without using the time-consuming correspondence optimization strategy. The aim of this master thesis is to classify the tree class from an image of a leaf with a computer vision classiffication system. We compare different descriptors that will describe the leaves different features. We will also look at different classiffication models and combine them with the descriptors to build a system hat could classify the different tree classes. Elliptic Fourier (EF) and discriminant analyses were used to identify young soybean (Glycine max (L.) merrill), sunflower (Helianthus pumilus), redroot pigweed (Amaranthus retroflexus) and velvetleaf (Abutilon theophrasti Medicus) plants, based on leaf shape. Chain encoded, Elliptic Fourier harmonic functions were generated based on leaf boundary. A complexity index of the leaf shape was computed using the variation between consecutive EF functions. Principle component analysis was used to select the Fourier coefficients with the best discriminatory power. Canonical discriminant analysis was used to develop species identification models based on leaf shapes extracted from plant color images during the second and third weeks after germination. The classification results showed that plant species during the third week were successfully identified with an average of correct classification rate of 89.4 . The discriminant model correctly classified on average: 77.9 of redroot pigweed, 93.8 of sunflower, 89.4 of velvetleaf and 96.5 of soybean. Using all of the leaves extracted from the second and the third weeks, the overall classification accuracy was 89.2 . The discriminant model correctly classified 76.4 of redroot pigweed, 93.6 of sunflower, 81.6 of velvetleaf, 91.5 of soybean leaf extracted from trifoliolate and 90.9 of soybean unifoliolate leaves. The Elliptic Fourier shape feature analysis could be an important and accurate tool for weed species identification and mapping.