关键词: 多核学习, 高效匹配核, 商品图像, 句子标注, 自然语言生成

Abstract:  Product characteristics can be described comprehensively by sentence as well as the information retrieval performance can be improved effectively because sentence contains rich semantic information. However, several problems such as insufficient feature learning and so simple feature still remain in current sentence annotation works. As the reason, image feature learning is implemented based on EMK (efficient match kernels) so that a shape EMK feature with more powerful discriminate ability is extracted to describe the product image. Moreover, shape, texture and gradient features are fused together to create a new feature named MKF (multiple kernel feature) by multiple kernel learning. MKF interprets the shape and texture characteristics of product image well. Finally, key texts are retrieved to annotate the product image after product image classification. The experimental results show that MKF achieves the best classification performance. Meanwhile products which have distinct shape and texture characteristics obtain better MAP (mean average precision) values. As is expected, BLEU (bilingual evaluation understudy) scores of the sentence generated by MK-SVM model are superior to the state of art baselines. More importantly, semantic information that the sentence contains is close to the product image’s content. Furthermore, the sentence is more coherent and readable than traditional models, which means high practicability.

Key words: multiple kernel learning, efficient match kernels, product image, sentence annotation, natural language generation