The Internet contains a multitude of social media posts and other of stories where text is interspersed with images. In these contexts, images are not simply used for general illustration, but are judiciously placed in certain spots of a story for multimodal descriptions and narration. In this work we analyze the problem of text-image alignment, and present SANDI, a methodology for automatically selecting images from an image collection and aligning them with text paragraphs of a story. SANDI combines visual tags, user-provided tags and background knowledge, and uses an Integer Linear Program to compute alignments that are semantically meaningful. Experiments show that SANDI can select and align images with texts with high quality of semantic fit.

Human perception is known to be predominantly visual. As modern web infrastructure promoted the storage of media, the web-data paradigm shifted from text-only documents to those containing text and images. A multitude of blog posts, news articles, and social media posts exist on the Internet today as examples of multimodal stories. The manual alignment of images and text in a story is time-consuming and labor intensive. We present a web application for automatically selecting relevant images from an album and placing them in suitable contexts within a body of text. The application solves a global optimization problem that maximizes the coherence of text paragraphs and image descriptors, and allows for exploring the underlying image descriptors and similarity metrics. Experiments show that our method can align images with texts with high semantic fit, and to user satisfaction.

The social media explosion has populated the Internet with a wealth of images. There are two existing paradigms for image retrieval: 1)content-based image retrieval (BIR), which has traditionally used visual features for similarity search (e.g., SIFT features), and 2) tag-based image retrieval (TBIR), which has relied on user tagging (e.g., Flickr tags). CBIR now gains semantic expressiveness by advances in deep-learning-based detection of visual labels. TBIR benefits from query-and-click logs to automatically infer more informative labels. However, learning-based tagging still yields noisy labels and is restricted to concrete objects, missing out on generalizations and abstractions. Click-based tagging is limited to terms that appear in the textual context of an image or in queries that lead to a click. This paper addresses the above limitations by semantically refining and expanding the labels suggested by learning-based object detection. We consider the semantic coherence between the labels for different objects, leverage lexical and commonsense knowledge, and cast the label assignment into a constrained optimization problem solved by an integer linear program. Experiments show that our method, called VISIR, improves the quality of the state-of-the-art visual labeling tools like LSDA and YOLO.

The widespread integration of cameras in hand-held and head-worn devices and the ability to share content online enables a large and diverse visual capture of the world that millions of users build up collectively every day. We envision these images as well as associated meta information, such as GPS coordinates and timestamps, to form a collective visual memory that can be queried while automatically taking the ever-changing context of mobile users into account. As a first step towards this vision, in this work we present Xplore-M-Ego: a novel media retrieval system that allows users to query a dynamic database of images using spatio-temporal natural language queries. We evaluate our system using a new dataset of real image queries as well as through a usability study. One key finding is that there is a considerable amount of inter-user variability in the resolution of spatial relations in natural language utterances. We show that our system can cope with this variability using personalisation through an online learning-based retrieval formulation.