欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
针对商品图像句子标注中图像特征单一、关键词受噪声干扰等问题,提出一种聚焦图像特征学习和关键词摘取的商品图像句子标注模型.从梯度、形状和颜色3个角度抽取图像核特征,并在多核学习模型内进行后融合.利用tag-rank模型中的绝对排序和相对排序特征提升关键词权重,设计词序列拼积木算法把关键词拼装成N元词序列.基于N元词序列和模板生成句子.实验表明：句子的BLEU-1和BLEU-2评分优于对比模型.
英文摘要：
Dealing with issues such as too simple image features and word noise inference in product image sentence anmotation, a product image sentence annotation model focusing on image feature learning and key words summarization is described. Three kernel descriptors such as gradient, shape, and color are extracted, respectively. Feature late-fusion is executed in turn by the multiple kernel learning model to obtain more discriminant image features. Absolute rank and relative rank of the tag-rank model are used to boost the key words＇ weights. A new word integration algorithm named word sequence blocks building （WSBB） is designed to create N-gram word sequences. Sentences are generated according to the N-gram word sequences and predefined templates. Experimental results show that both the BLEU-1 scores and BLEU-2 scores of the sentences are superior to those of the state-of-art baselines.
同期刊论文项目
同项目期刊论文
期刊信息
位置：