计算机科学与探索2015，Vol.9Issue(11)：1351-1361,11.DOI:10.3778/j.issn.1673-9418.1503017
基于多核学习的商品图像句子标注
Product Image Sentence Annotation Based on Multiple Kernel Learning
摘要
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.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Key words
multiple kernel learning/efficient match kernels/product image/sentence annotation/natural language generation分类
信息技术与安全科学引用本文复制引用
张红斌,姬东鸿,任亚峰,尹兰..基于多核学习的商品图像句子标注[J].计算机科学与探索,2015,9(11):1351-1361,11.基金项目
The National Natural Science Foundation of China under Grant No. 61133012 (国家自然科学基金重点项目) （国家自然科学基金重点项目）
the Humanity and Social Science Foundation of the Ministry of Education of China under Grant Nos. 12YJCZH274, 11YJC870012 (教育部人文社科基金) （教育部人文社科基金）
the Key Science and Technology Program of Jiangxi Province under Grant Nos. 20142BBG70011, 20121BBG70050 (江西省科技攻关计划项目). （江西省科技攻关计划项目）
