Abstract
The zero-shot classification algorithm has been widely concerned in recent years, in which the labeling of samples of a new category is unnecessary and the cost of annotations can be reduced in applications. This paper presents a zero-shot method for image classification based on word vectors enhancement and distance metric learning. Specifically, the convolutional neural network (CNN) is employed to extract image feature vectors which have the same dimension as semantic feature vectors. Then, an unsupervised learning method is applied on Wikipedia corpus for extracting word vectors and the skip-gram is used to obtain word vectors. The model of analysis dictionary learning is improved by reducing redundant information in word vectors. The obtained sparse vectors are used as semantic features and a distance metric learning method is employed to measure the distance between image features and semantic features. Finally, the classification is implemented by a nearest neighbor based classifier. The effectiveness of the proposed algorithm is validated on the AwA and CUB data sets. Experimental results demonstrate that the proposed method has good performance in terms of both accuracy and robustness.
Highlights
Most of the existing object classification methods are within the scope of supervised learning
3 that the performance of the analysis dictionary learning (ADL)-distance metric learning (DML) method has been significantly improved compared with the Euc method
Based on word vector enhancement and distance metric learning, this paper proposes a zero-shot image classification method, enhancing the accuracy of classification and overcoming the limitation of attribute learning, not necessarily labeling a large amount of data
Summary
Most of the existing object classification methods are within the scope of supervised learning. The core idea of the embedding model method is to simultaneously map all visual features and category labels to a certain space, and perform zero-shot classification based on the similarity measure [16], [17]. VOLUME 8, 2020 configured in equal importance at this time, the relationship between samples cannot be effectively described In this case, this paper uses distance metric learning (DML) to measure the distance between the image feature vector and the semantic feature vector, and uses the nearest neighbor classifier to classify depending upon the distance. 3) A zero-shot image classification method based on word vector enhancement and distance metric learning is proposed, which acquires better performance in accuracy and robustness than several mainstream ZSL methods
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