Multi-label image classification aims to predict a set of labels for a given input image. Existing studies based on semantic information either use the correlation between semantic and visual space to guide the feature extraction process to generate effective feature representations or use the correlation between semantic and label spaces to learn weighted classifiers that capture label correlation. Most of these works use semantic information as auxiliary information for exploiting the visual space or label space, and few studies have exploited semantic, visual, and label space correlations simultaneously. However, these approaches fail to model the correlations across semantic, visual, and label spaces simultaneously. To solve this problem, a semantic information-guided multi-label image classification (SIG-MLIC) method was proposed. SIG-MLIC could simultaneously utilize semantic, visual, and label spaces, generating semantically specific feature representations via the association of image regions with labels reinforced by a semantic-guided attention (SGA) mechanism. Besides, the semantic information of labels was used to generate a semantic dictionary with label relevance constraints to reconstruct visual features, obtaining normalized representation coefficients as the probability of label occurrence. Experimental results on three standard multi-label image classification datasets show that both the attention mechanism and dictionary learning in SIG-MLIC can effectively improve classification performance, and the effectiveness of the proposed method has been verified.

The problem of imbalanced data classification learning has received much attention. Conventional classification algorithms are susceptible to data skew to favor majority samples and ignore minority samples. Majority weighted minority oversampling technique (MWMOTE) is an effective approach to solve this problem, however, it may suffer from the shortcomings of inadequate noise filtering and synthesizing the same samples as the original minority data. To this end, we propose an improved MWMOTE method named joint sample position based noise filtering and mean shift clustering (SPMSC) to solve these problems. Firstly, in order to effectively eliminate the effect of noisy samples, SPMSC uses a new noise filtering mechanism to determine whether a minority sample is noisy or not based on its position and distribution relative to the majority sample. Note that MWMOTE may generate duplicate samples, we then employ the mean shift algorithm to cluster minority samples to reduce synthetic replicate samples. Finally, data cleaning is performed on the processed data to further eliminate class overlap. Experiments on extensive benchmark datasets demonstrate the effectiveness of SPMSC compared with other sampling methods.