Automatically creating the description of an image using any natural languages sentence like English is a very challenging task. It requires expertise of both image processing as well as natural language processing. This paper discuss about different available models for image captioning task. We have also discussed about how the advancement in the task of object recognition and machine translation has greatly improved the performance of image captioning model in recent years. In addition to that we have discussed how this model can be implemented. In the end, we have also evaluated the performance of model using standard evaluation matrices.
@cite_10: We develop and demonstrate automatic image description methods using a large captioned photo collection. One contribution is our technique for the automatic collection of this new dataset – performing a huge number of Flickr queries and then filtering the noisy results down to 1 million images with associated visually relevant captions. Such a collection allows us to approach the extremely challenging problem of description generation using relatively simple non-parametric methods and produces surprisingly effective results. We also develop methods incorporating many state of the art, but fairly noisy, estimates of image content to produce even more pleasing results. Finally we introduce a new objective performance measure for image captioning.
There are mainly two different way to perform the task of image captioning. These two types are basically retrieval based method and generative method. From that most of work is done based on retrieval based method. One of the best model of retrieval based method is Im2Txt model @cite_10 . It was proposed by Vicente Ordonez, Girish Kulkarni and Tamara L Berg. Their system is divided into mainly two part 1) Image matching and 2) Caption generation. First we will provide our input image to model. Matching image will be retrieved from database containing images and its appropriate caption. Once we find matching images we will compare extracted high level objects from original image and matching images. Images will then reranked based on the content matched. Once it is reranked caption of top-n ranked images will be returned. The main limitation of these retrieval based method is that it can only produce captions which are already present in database. It can not generate novel captions.