{'fraction_non_alphanumeric': 0.02975724353954581, 'fraction_numerical': 0.0015661707126076742, 'mean_word_length': 5.326732673267327, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Collaborating CNN and SVM for Automatic Image Annotation

To learn a well-performed image annotation model, a large number of labeled samples are usually required. In this paper, we propose a novel semi-supervised approach based on adaptive weighted fusion for automatic image annotation, which can utilize the labeled data and unlabeled data simultaneously. Firstly, two different classifiers, namely the CNN (convolutional neural network) and the LDA-SVM, are constructed by all the labeled data. These two classifiers are independently represented as different feature views. Then, the most confident data with relevant pseudo-labels are chosen and amalgamated with the whole labeled dataset. After that, the two classifiers are retrained with the new labeled dataset until a stop condition is reached. In each iteration process, the unlabeled samples are labeled by high confidence pseudo-labels that are estimated by an adaptive weighted fusion strategy. Finally, we conduct experiments on two datasets, namely IAPR TC12 and NUS-WIDE, and measure the performance of the model with standard criteria, including precision, recall, F-measure, N+ and mAP. The experimental results show that our approach outperforms many state-of-the-art automatic image annotation approaches.