text: In this paper, a 1d convolutional neural network is designed for classification tasks of leaves with centroid contour distance curve (CCDC) as the single feature. With this classifier, simple feature as CCDC shows more discriminating power than people thought previously. The same architecture can also be applied for classifying 1 dimensional time series with little changes. Experiments on some benchmark datasets shows this architecture can provide classification accuracies that are higher than some existing methods. Code for the paper is available at this https URL Project.
  @cite_0: We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5 and 17.0 which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overriding in the fully-connected layers we employed a recently-developed regularization method called "dropout" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3 , compared to 26.2 achieved by the second-best entry.
  @cite_8: Large Convolutional Network models have recently demonstrated impressive classification performance on the ImageNet benchmark [18]. However there is no clear understanding of why they perform so well, or how they might be improved. In this paper we explore both issues. We introduce a novel visualization technique that gives insight into the function of intermediate feature layers and the operation of the classifier. Used in a diagnostic role, these visualizations allow us to find model architectures that outperform on the ImageNet classification benchmark. We also perform an ablation study to discover the performance contribution from different model layers. We show our ImageNet model generalizes well to other datasets: when the softmax classifier is retrained, it convincingly beats the current state-of-the-art results on Caltech-101 and Caltech-256 datasets.
summary: Compared with methods mentioned above which tackles the difficulty in classification by designing complicated hand crafted deep features, convolutional neural networks (CNN) @cite_0 can take simple features as input and automatically abstracts useful features through its early convolutional blocks for later classification tasks @cite_8 . In this way, the difficulty is transferred into heavy computation where modern hardware now can provide sufficient support. It is more straightforward if we apply a CNN directly on leave images combining feature extraction task and classification task together, but this will make a model of unnecessary large size with a lot of parameters and they usually require a lot of data and time to be trained well with more risk of overfitting the data at hand. The key idea of this paper is to take the advantage of convolutional architecture, but apply it on the extracted single 1d CCDC feature to reduce the computational cost.