Image distance metric learning based on neighborhood sets for automatic image annotation

• Knowledge of the samples with and without caption is sufficiently considered.
• The number of labels is completely determined by the image content.
• The proposed AIA approach can automatically implemented.

Since there is semantic gap between low-level visual features and high-level image semantic, the performance of many existing content-based image annotation algorithms is not satisfactory. In order to bridge the gap and improve the image annotation performance, a novel automatic image annotation (AIA) approach using neighborhood set (NS) based on image distance metric learning (IDML) algorithm is proposed in this paper. According to IDML, we can easily obtain the neighborhood set of each image since obtained image distance can effectively measure the distance between images for AIA task. By introducing NS, the proposed AIA approach can predict all possible labels of the image without caption. The experimental results confirm that the introduction of NS based on IDML can improve the efficiency of AIA approaches and achieve better annotation performance than the existing AIA approaches.

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