to handle implicit relations like content similarities, and explicit ones like friendship or authorship. It can be used either for fully automatic image labeling or for helping the user with a ranked list of candidate tags. The model itself is based on a transductive algorithm thats learns from both labeled

We propose a relational ranking model for learning to tag images in social media sharing systems. This model learns to associate a ranked list of tags to unlabeled images, by considering simultaneously content information (visual or textual) and relational information among the images. It is able

We seek to build a large collection of images with ground truth labels to be used for object detection and recognition research. Such data is useful for supervised learning and quantitative evaluation. To achieve this, we developed a web-based tool that allows easy image annotation and instant

We present a system for identifying the semantic relationships, or semantic roles, filled by constituents of a sentence within a semantic frame. Various lexical and syntactic features are derived from parse trees and used to derive statistical classifiers from hand-annotated training data. 1

on content. Here, we propose an automatic approach to annotating and retrieving images based on a training set of images. We assume that regions in an image can be described using a small vocabulary of blobs. Blobs are generated from image features using clustering. Given a training set of images

We consider the problem of modeling annotated data—data with multiple types where the instance of one type (such as a caption) serves as a description of the other type (such as an image). We describe three hierarchical probabilistic mixture models that are aimed at such data, culminating

of similar regions. Prior to the queries, the system automatically extracts and indexes salient color regions from the images. By utilizing efficient indexing techniques for color information, region sizes and absolute and relative spatial locations, a wide variety of complex joint color/spatial queries may

and to analyze the frequency of syntactic/semantic alternations in the corpus. We describe an automatic system for semantic role tagging trained on the corpus and discuss the effect on its performance of various types of information, including a comparison of full syntactic parsing with a flat representation

The intuitive background for measures of structural centrality in social networks is reviewed aPzd existing measures are evaluated in terms of their consistency with intuitions and their interpretability. Three distinct intuitive conceptions of centrality are uncovered and existing measures

What was noted by E. J. hanger (1978) remains true today: that much of contemporary psychological research is based on the assumption that people are consciously and systematically processing incoming information in order to construe and interpret their world and to plan and engage in courses