Understanding and predicting importance in images
What do people care about in an image? To drive computational visual recognition toward more human-centric outputs, we need a better understanding of how people perceive and judge the importance of content in images. In this paper, we explore how a number of factors relate to human perception of importance. Proposed factors fall into 3 broad types: 1) factors related to composition, e.g. size, location, 2) factors related to semantics, e.g. category of object or scene, and 3) contextual factors related to the likelihood of attribute-object, or object-scene pairs. We explore these factors using what people describe as a proxy for importance. Finally, we build models to predict what will be described about an image given either known image content, or image content estimated automatically by recognition systems.
Fast growth of search engines' popularity shows the users attraction to the Web engines. However there is a chance of misinterpretation for ambiguous queries. At this point, we propose a more adherence user interface which consist of a relevant visual content as well as generating new search snippet and title. Recent researches for meeting this aim are focused on a whole page thumbnail for assisting users to remember a recently visited Web page. Withal, this is not discussed yet that how a specific visual content of a page can allow users to distinguish between a useful and worthless page in the result page especially in an ambiguous search task. Our studya shows that the improvement in both textual search snippet and title as well as the additional thumbnail were helpful for users to clarify the Search Engine Result Page (SERP) in an ambiguous search task.