Knowledge-rich Image Gist Understanding Beyond Literal Meaning

We investigate the problem of understanding the message (gist) conveyed by images and their captions as found, for instance, on websites or news articles. To this end, we propose a methodology to capture the meaning of image-caption pairs on the basis of large amounts of machine-readable knowledge that has previously been shown to be highly effective for text understanding. Our method identifies the connotation of objects beyond their denotation: where most approaches to image understanding focus on the denotation of objects, i.e., their literal meaning, our work addresses the identification of connotations, i.e., iconic meanings of objects, to understand the message of images. We view image understanding as the task of representing an image-caption pair on the basis of a wide-coverage vocabulary of concepts such as the one provided by Wikipedia, and cast gist detection as a concept-ranking problem with image-caption pairs as queries. To enable a thorough investigation of the problem of gist understanding, we produce a gold standard of over 300 image-caption pairs and over 8,000 gist annotations covering a wide variety of topics at different levels of abstraction. We use this dataset to experimentally benchmark the contribution of signals from heterogeneous sources, namely image and text. The best result with a Mean Average Precision (MAP) of 0.69 indicate that by combining both dimensions we are able to better understand the meaning of our image-caption pairs than when using language or vision information alone. We test the robustness of our gist detection approach when receiving automatically generated input, i.e., using automatically generated image tags or generated captions, and prove the feasibility of an end-to-end automated process

Understanding the message of images

We investigate the problem of understanding the message (gist) conveyed by images and their captions as found, for instance, on websites or news articles. To this end, we propose a methodology to capture the meaning of image-caption pairs on the basis of large amounts of machine-readable knowledge that have previously been shown to be highly effective for text understanding. Our method identifies the connotation of objects beyond their denotation: where most approaches to image or image-text understanding focus on the denotation of objects, i.e., their literal meaning, our work addresses the identification of connotations, i.e., iconic meanings of objects, to understand the message of images. We view image understanding as the task of representing an image-caption pair on the basis of a widecoverage vocabulary of concepts such as the one provided by Wikipedia, and cast gist detection as a concept-ranking problem with image-caption pairs as queries

Requirements elicitation towards a search engine for semantic multimedia content

We investigate user requirements regarding the interface design for a semantic multimedia search and retrieval based on a prototypical implementation of a search engine for multimedia content on the web. Thus, unlike existing image search engines and video search engines, we are interested in true multimedia content combining different media assets into multimedia documents like PowerPoint presentations and Flash files. In a user study with 20 participants, we conducted a formative evaluation based on the think-aloud method and semi-structured interviews in order to obtain requirements to a future web search engine for multimedia content. The interviews are complemented by a paper-and-pencil questionnaire to obtain quantitative information and present mockups demonstrating the user interface of a future multimedia search and retrieval engine

fulgeo-Design of an Intuitive User Interface for a Multimedia Search Engine

Multimedia documents like PowerPoint presentations or Flash documents are widely adopted in the Internet and exist in context of lots of different topics. However, so far there is no user friendly way to explore and search for this content. The aim of this work is to address this issue by developing a new, easy-to-use user interface approach and prototype search engine. Our system is called fulgeo and specifically focuses on a suitable multimedia interface for visualizing the query results of Flash documents. The prototype is available online as live demo at: http://fulgeo.komsys.org

Fulgeo - towards an intuitive user interface for a semantics-enabled multimedia search engine

Multimedia documents like PowerPoint presentations or Flash documents are widely adopted in the Internet and exist in context of lots of different topics. However, so far there is no user friendly way to explore and search for this content. The aim of this work is to address this issue by developing a new, easy-to-use user interface approach and prototype search engine. Our system is called fulgeo and specifically focuses on a suitable multimedia interface for visualizing the query results of semantically-enriched Flash documents

Weakly supervised construction of a repository of iconic images

We present a first attempt at semi-automatically harvesting a dataset of iconic images, namely images that depict objects or scenes, which arouse associations to abstract topics. Our method starts with representative topic-evoking images from Wikipedia, which are labeled with relevant concepts and entities found in their associated captions. These are used to query an online image repository (i.e., Flickr), in order to further acquire additional examples of topic-specific iconic relations. To this end, we leverage a combination of visual similarity measures, image clustering and matching algorithms to acquire clusters of iconic images that are topically connected to the original seed images, while also allowing for various degrees of diversity. Our first results are promising in that they indicate the feasibility of the task and that we are able to build a first version of our resource with minimal supervision

Data from the paper: Weakly supervised construction of a repository of iconic images

We present a first attempt at semi-automatically harvesting a dataset of iconic images. Iconic images are depicting objects or scenes, which arouse associations to abstract topics. Our method starts with representative topic-evoking images from Wikipedia, which are labeled with relevant concepts and entities found in their associated captions. These are used to query an online image repository (i.e., Flickr), in order to further acquire additional examples of topic-specific iconic relations. To this end, we leverage a combination of visual similarity measures, image clustering and matching algorithms to acquire clusters of iconic images that are topically connected to the original seed images, while also allowing for various degrees of diversity. Our first results are promising in that they indicate the feasibility of the task and that we are able to build a first version of our resource with minimal supervision

Image with a Message: Towards Detecting Non-Literal Image Usages by Visual Linking

A key task to understand an image and its corresponding caption is not only to find out what is shown on the picture and described in the text, but also what is the exact relationship between these two elements. The long-term objective of our work is to be able to distinguish different types of relationship, including literal vs. non-literal usages, as well as finegrained non-literal usages (i.e., symbolic vs. iconic). Here, we approach this challenging problem by answering the question: ‘How can we quantify the degrees of similarity between the literal meanings expressed within images and their captions?’. We formulate this problem as a ranking task, where links between entities and potential regions are created and ranked for relevance. Using a Ranking SVM allows us to leverage from the preference ordering of the links, which help us in the similarity calculation for the cases of visual or textual ambiguity, as well as misclassified data. Our experiments show that aggregating different features using a supervised ranker achieves better results than a baseline knowledge-base method. However, much work still lies ahead, and we accordingly conclude the paper with a detailed discussion of a short- and longterm outlook on how to push our work on relationship classification one step further

Image with a message : towards detecting non-literal image usages by visual linking

A key task to understand an image and its corresponding caption is not only to find out what is shown on the picture and described in the text, but also what is the exact relationship between these two elements. The long-term objective of our work is to be able to distinguish different types of relationship, including literal vs. non-literal usages, as well as finegrained non-literal usages (i.e., symbolic vs. iconic). Here, we approach this challenging problem by answering the question: ‘How can we quantify the degrees of similarity between the literal meanings expressed within images and their captions?’. We formulate this problem as a ranking task, where links between entities and potential regions are created and ranked for relevance. Using a Ranking SVM allows us to leverage from the preference ordering of the links, which help us in the similarity calculation for the cases of visual or textual ambiguity, as well as misclassified data. Our experiments show that aggregating different features using a supervised ranker achieves better results than a baseline knowledge-base method. However, much work still lies ahead, and we accordingly conclude the paper with a detailed discussion of a short- and longterm outlook on how to push our work on relationship classification one step further