This article examines what is being promoted about Tianjin’s rich heritage through its tourism and heritage practices. An industrial city traditionally known for its crafts and gastronomy, Tianjin has gradually begun to promote its Chinese heritage and, since the 2000s, its city centre, which is noteworthy for its former foreign concessions and 19th- and 20th-century architectural heritage. After long neglect, the city centre has become a major component in the promotion of the city. Based on the analysis of tourism materials and studies conducted since the mid-2000s, this article first discusses the development of tourism in Tianjin and the chronology and interweaving of the services involved in heritage development. Then, it focuses on the reception of the city’s tourism offerings and on urban development operations in two sectors, Wudadao (or ‘Five avenues’), located in the former British Concession, and the ‘I-Style Town’ in the former Italian Concession.
Fungal diseases are the major diseases of agricultural production, and have brought tremendous impact to it. Identification of spore morphology plays an important role in the identification of fungi. This paper uses the microscopy images of two kinds of fungal spore and utilizes the technology of image analysis and recognition to classify them. We firstly get the underlying feature descriptors of these two kinds of microscopy images by RGB SIFT (Scale Invariant Feature Transform), then create the visual word dictionary using K-means clustering algorithm, at finally we use LDA, KNN and SVM to classify these two kinds of images. The results indicate that the classification of spore image based on feature extraction is feasible. In our future work, we will conduct the classification of related species and highly similar spore images.
In this paper, a novel image classification method, incorporating active learning and semi-supervised learning (SSL), is proposed. In this method, two classifiers are needed where one is trained by labeled data and some unlabeled data, while the other one is trained only by labeled data. Specifically, in each round, two classifiers iterate to select useful examples in contention for user query. Then we compute the label changing rate for every unlabeled example in each classifier. Those examples in which the label changing rate is zero and the label in the two classifiers is the same are selected to add into the training data of the first classifier. Our experimental results show that our method significantly reduced the need of labeled examples, while at the same time reducing classification error compared with widely used image classification algorithms.
We use deep max-pooling convolutional neural networks to address a problem of neuroanatomy, namely, the automatic segmentation of cerebral cortex structures of laboratory rat depicted in stacks of Two-photon microscopy images and detect the change areas when stimulation occurs. We classify each pixel in the image by training a CNN network, using a square window to predict the probability of the central pixel for each class. After classification, we perform the post-processing on the output produced by CNN. At last, we depict the areas that we interested through a threshold value.
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