Capturing the interesting components of an image is a key aspect of image understanding. When a speaker annotates an image, selecting labels that are informative greatly depends on the prior knowledge of a prospective listener. Motivated by cognitive theories of categorization and communication, we present a new unsupervised approach to model this prior knowledge and quantify the informativeness of a description. Specifically, we compute how knowledge of a label reduces uncertainty over the space of labels and utilize this to rank candidate labels for describing an image. While the full estimation problem is intractable, we describe an efficient algorithm to approximate entropy reduction using a tree-structured graphical model. We evaluate our approach on the open-images dataset using a new evaluation set of 10K ground-truth ratings and find that it achieves 65 agreement with human raters, largely outperforming other unsupervised baseline approaches.
* Image importance and object saliency. The problem of deciding which components in an image are important has been studied intensively. The main approaches involved identifying characteristics of objects and images that could contribute to importance, and use labeled data for predicting object importance. Elazary and Itti @cite_8 considered the order of object naming in the LabelMe dataset @cite_7 as a measure of the interest of an object and compare that to salient locations predicted by computational models of bottom-up attention. The elegant work of Spain and Perona @cite_18 examined which factors can predict the order in which objects will be mentioned given an image. @cite_19 characterized factors related to semantics, to composition and to the likelihood of attribute-object, and investigated how these affected the measures of importance. @cite_6 focused on predicting entry-level classes using a supervised approach. These studies also make it clear that the object saliency is strongly correlated with its perceived importance @cite_16 @cite_4 .
@cite_18 @cite_4 @cite_7 @cite_8 @cite_6 @cite_19 @cite_16
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How do we decide which objects in a visual scene are more interesting? While intuition may point toward high-level object recognition and cognitive processes, here we investigate the contributions of a much simpler process, low-level visual saliency. We used the LabelMe database (24,863 photographs with 74,454 manually outlined objects) to evaluate how often interesting objects were among the few most salient locations predicted by a computational model of bottom-up attention. In 43 of all images the model’s predicted most salient location falls within a labeled region (chance 21 ). Furthermore, in 76 of the images (chance 43 ), one or more of the top three salient locations fell on an outlined object, with performance leveling off after six predicted locations. The bottom-up attention model has neither notion of object nor notion of semantic relevance. Hence, our results indicate that selecting interesting objects in a scene is largely constrained by low-level visual properties rather than solely determined by higher cognitive processes. 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. In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors' interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Moreover, we hypothesize that apart from the citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text. A large body of previous models to predict where people look in natural scenes focused on pixel-level image attributes. To bridge the semantic gap between the predictive power of computational saliency models and human behavior, we propose a new saliency architecture that incorporates information at three layers: pixel-level image attributes, object-level attributes, and semantic-level attributes. Object- and semantic-level information is frequently ignored, or only a few sample object categories are discussed where scaling to a large number of object categories is not feasible nor neurally plausible. To address this problem, this work constructs a principled vocabulary of basic attributes to describe object- and semantic-level information thus not restricting to a limited number of object categories. We build a new dataset of 700 images with eye-tracking data of 15 viewers and annotation data of 5,551 segmented objects with fine contours and 12 semantic attributes (publicly available with the paper). Experimental results demonstrate the importance of the object- and semantic-level information in the prediction of visual attention.