This paper describes some fusion techniques for achieving high accuracy species identification from images of different plant organs. Given a series of different image organs such as branch, entire, flower, or leaf, we firstly extract confidence scores for each single organ using a deep convolutional neural network. Then, various late fusion approaches including conventional transformation-based approaches (sum rule, max rule, product rule), a classification-based approach (support vector machine), and our proposed hybrid fusion model are deployed to determine the identity of the plant of interest. For single organ identification, two schemes are proposed. The first scheme uses one Convolutional neural network (CNN) for each organ while the second one trains one CNN for all organs. Two famous CNNs (AlexNet and Resnet) are chosen in this paper. We evaluate the performances of the proposed method in a large number of images of 50 species which are collected from two primary resources: PlantCLEF 2015 dataset and Internet resources. The experiment exhibits the dominant results of the fusion techniques compared with those of individual organs. At rank-1, the highest species identification accuracy of a single organ is 75.6% for flower images, whereas by applying fusion technique for leaf and flower, the accuracy reaches to 92.6%. We also compare the fusion strategies with the multi-column deep convolutional neural networks (MCDCNN) [1]. The proposed hybrid fusion scheme outperforms MCDCNN in all combinations. It obtains from + 3.0% to + 13.8% improvement in rank-1 over MCDCNN method. The evaluation datasets as well as the source codes are publicly available. Keywords: Plant identification, Convolutional neural network, Deep learning, Fusion.
Automatic license plate recognition and vehicle classification based on computer vision technique have been deeply studied and developed. Certain commercial systems have been deployed. This paper aims at making a review of the approaches for license plate recognition and weaknesses of each approach are also pointed out. This paper can be used as an reference while developing alicense plate recognition and vechicle classification system.
Along with the strong development of camera networks, a video analysis system has been become more and more popular and has been applied in various practical applications. In this paper, we focus on person re-identification (person ReID) task that is a crucial step of video analysis systems. The purpose of person ReID is to associate multiple images of a given person when moving in a non-overlapping camera network. Many efforts have been made to person ReID. However, most of studies on person ReID only deal with well-alignment bounding boxes which are detected manually and considered as the perfect inputs for person ReID. In fact, when building a fully automated person ReID system the quality of the two previous steps that are person detection and tracking may have a strong effect on the person ReID performance. The contribution of this paper are two-folds. First, a unified framework for person ReID based on deep learning models is proposed. In this framework, the coupling of a deep neural network for person detection and a deep-learning-based tracking method is used. Besides, features extracted from an improved ResNet architecture are proposed for person representation to achieve a higher ReID accuracy. Second, our self-built dataset is introduced and employed for evaluation of all three steps in the fully automated person ReID framework.
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