• RINKU BALAN K
• LISSY ANTO P
• GREESHMA SUNNY

Captions are essential components associated with images to make search engines to respond easily with user queries. Making appropriate captions for images is a difficult task. By making the appropriate caption will help the user to search images with long queries. But most of the images are associated with user annotated tags, captions and text surrounding the images. This paper is concerned with the task of automatic caption generation for news images in association with the related news article. In this method we will input one image and news article to the system. The system will generate most important keywords which are associated with the image. And using these features we will compare the image with the images which are stored in the database. After finding the best matched image we will extract the keywords associated with that image. After applying grammatical rules to the keywords an appropriate caption is generated. Here we are combing the textual modalities with the visual one. In the existing method the captions are not efficiently generated and there is no mapping between the image and the text associated with it. But we are introducing a best method for news image caption generation without costly manual involvement. Specifically, we exploit data resources where images and their textual descriptions co-occur naturally. We present a new dataset consisting of news articles, images, and their captions that we required from the BBC News website. Rather than laboriously annotating images with keywords, we simply treat the captions as the labels. We show that it is possible to learn the visual and textual correspondence under such noisy conditions by extending an existing generative annotation mode. We also find that the accompanying news documents substantially complements the extraction of the image content. In order to provide a better modelling and representation of image content, We propose a probabilistic image annotation model that exploits the synergy between visual and textual modalities under the assumption that images and their textual descriptions are generated by a shared set of latent variables. Using Latent Dirichlet Allocation, we represent visual and textual modalities jointly as a probability distribution over a set of topics. Our model takes these topic distributions into account while finding the most likely keywords for an image and its associated document. The availability of news documents in our dataset allows us to perform the caption generation task in a fashion akin to text summarization; save one important difference that our model is not solely based on text but uses the image in order to select content from the document that should be present in the caption. We propose both extractive and abstractive caption generation models to render the extracted image content in natural language without relying on rich knowledge resources, sentence-templates or grammars. The backbone for both approaches is our topic-based image annotation model. Our extractive models examine how to best select sentences that overlap in content with our image annotation model. We modify an existing abstractive headline generation model to our scenario by incorporating visual information. Our own model operates over image description keywords and document phrases by taking dependency and word order constraints into account. Experimental results show that both approaches can generate human-readable captions for news images. Our phrase-based abstractive model manages to yield as informative captions as those written by the BBC journalists.

Caption generation, image annotation, summarization.

"Automatic Caption Generation For News Images", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.2, Issue 7, page no.3108-3110, July-2015, Available :http://www.jetir.org/papers/JETIR1507022.pdf

"Automatic Caption Generation For News Images", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.2, Issue 7, page no. pp3108-3110, July-2015, Available at : http://www.jetir.org/papers/JETIR1507022.pdf