  The image captioning task is about to generate suitable descriptions from
images. For this task there can be several challenges such as accuracy, fluency
and diversity. However there are few metrics that can cover all these
properties while evaluating results of captioning models.In this paper we first
conduct a comprehensive investigation on contemporary metrics. Motivated by the
auto-encoder mechanism and the research advances of word embeddings we propose
a learning based metrics for image captioning, which we call Intrinsic Image
Captioning Evaluation(I2CE). We select several state-of-the-art image
captioning models and test their performances on MS COCO dataset with respects
to both contemporary metrics and the proposed I2CE. Experiment results show
that our proposed method can keep robust performance and give more flexible
scores to candidate captions when encountered with semantic similar expression
or less aligned semantics. On this concern the proposed metric could serve as a
novel indicator on the intrinsic information between captions, which may be
complementary to the existing ones.
