Computer Science ›› 2016, Vol. 43 ›› Issue (5): 269-273, 287.doi: 10.11896/j.issn.1002-137X.2016.05.051

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Product Image Sentence Annotation Based on Gradient Kernel Feature and N-gram Model

ZHANG Hong-bin, JI Dong-hong, YIN Lan and REN Ya-feng   

  • Online:2018-12-01 Published:2018-12-01

Abstract: Product image sentence annotation was presented because sentence describes online products more accurately than single words.Firstly,image feature learning was executed.Gradient kernel feature that achieves the best annotation performance was chosen because the feature describes the key visual characteristics of product image such as shape and texture better than other features.Therefore,the gradient kernel feature was selected to complete image classification and image retrieval.Secondly,several key words were summarized from training images’ captions based on semantic correlation computing.Thirdly,a modified sequence that not only contains rich semantic information but also satisfies syntactic mode compatibility was created based on these key words by N-gram model.Sentence was generated according to predefined sentence template and the modified sequence.Finally,a Boosting model was designed to choose those sentences that obtain the best BLEU-3 scores to annotate product images.Experiments show sentences generated by the boosting model achieve the state of art annotation performances.

Key words: Gradient kernel feature,N-gram model,Product image,Sentence annotation,Semantic correlation computing,Modified sequence

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