80,282 research outputs found
Writing with Image: Verbal-Visual Collaboration in 20th-Century Poetry
This study examines verbal-visual collaboration in which a poet and a visual artist work cooperatively to produce a single book. Verbal-visual collaboration is a fertile genre that refigures the historically oppositional relationship between the sister arts and that anticipates today's hypertext experiments in interart forms. I confront the problem of reading a multi-media text and posit “integrated reading” as a constructive critical approach that privileges neither word nor image. Integrated reading stresses relationships and asks questions about how the verbal and visual elements interact, what they say to and about each other, and how they work together to interrogate issues of representation. Examining the nature of poetry from the stance of images, and vice versa, means questioning the nature of representation itself. A central concern of verbal-visual collaborations, and modern poetry, is representation. My integrated readings consider issues of representation demonstrated in the process, presentation, and meaning-making of verbal-visual collaborations. My dissertation has two other goals: to begin to write the history of modern verbal-visual collaboration and to develop a taxonomy of such projects. I focus on three texts: Ted Hughes and Leonard Baskin's Capriccio (1990), Frank O'Hara and Larry Rivers' Stones (1957-1960), and C.D. Wright and Deborah Luster's One Big Self: Prisoners of Louisiana (2004). I trace the specific histories of these works to position each within the history of verbal-visual collaboration and to show how the creative process bears on reading a collaborative text. I describe categories of collaboration based on the working proximity of artist and poet and their relationship to material production, and my taxonomy provides a beginning for classifying the various ways in which poets interact with visual images
Utilising semantic technologies for intelligent indexing and retrieval of digital images
The proliferation of digital media has led to a huge interest in classifying and indexing media objects for generic search and usage. In particular, we are witnessing colossal growth in digital image repositories that are difficult to navigate using free-text search mechanisms, which often return inaccurate matches as they in principle rely on statistical analysis of query keyword recurrence in the image annotation or surrounding text. In this paper we present a semantically-enabled image annotation and retrieval engine that is designed to satisfy the requirements of the commercial image collections market in terms of both accuracy and efficiency of the retrieval process. Our search engine relies on methodically structured ontologies for image annotation, thus allowing for more intelligent reasoning about the image content and subsequently obtaining a more accurate set of results and a richer set of alternatives matchmaking the original query. We also show how our well-analysed and designed domain ontology contributes to the implicit expansion of user queries as well as the exploitation of lexical databases for explicit semantic-based query expansion
Hierarchy-based Image Embeddings for Semantic Image Retrieval
Deep neural networks trained for classification have been found to learn
powerful image representations, which are also often used for other tasks such
as comparing images w.r.t. their visual similarity. However, visual similarity
does not imply semantic similarity. In order to learn semantically
discriminative features, we propose to map images onto class embeddings whose
pair-wise dot products correspond to a measure of semantic similarity between
classes. Such an embedding does not only improve image retrieval results, but
could also facilitate integrating semantics for other tasks, e.g., novelty
detection or few-shot learning. We introduce a deterministic algorithm for
computing the class centroids directly based on prior world-knowledge encoded
in a hierarchy of classes such as WordNet. Experiments on CIFAR-100, NABirds,
and ImageNet show that our learned semantic image embeddings improve the
semantic consistency of image retrieval results by a large margin.Comment: Accepted at WACV 2019. Source code:
https://github.com/cvjena/semantic-embedding
Wikipedia as an encyclopaedia of life
In his 2003 essay E O Wilson outlined his vision for an “encyclopaedia of life” comprising “an electronic page for each species of organism on Earth”, each page containing “the scientific name of the species, a pictorial or genomic presentation of the primary type specimen on which its name is based, and a summary of its diagnostic traits.” Although the “quiet revolution” in biodiversity informatics has generated numerous online resources, including some directly inspired by Wilson's essay (e.g., "http://ispecies.org":http://ispecies.org, "http://www.eol.org":http://www.eol.org), we are still some way from the goal of having available online all relevant information about a species, such as its taxonomy, evolutionary history, genomics, morphology, ecology, and behaviour. While the biodiversity community has been developing a plethora of databases, some with overlapping goals and duplicated content, Wikipedia has been slowly growing to the point where it now has over 100,000 pages on biological taxa. My goal in this essay is to explore the idea that, largely independent of the efforts of biodiversity informatics and well-funded international efforts, Wikipedia ("http://en.wikipedia.org/wiki/Main_Page":http://en.wikipedia.org/wiki/Main_Page) has emerged as potentially the best platform for fulfilling E O Wilson’s vision
Evaluation of Output Embeddings for Fine-Grained Image Classification
Image classification has advanced significantly in recent years with the
availability of large-scale image sets. However, fine-grained classification
remains a major challenge due to the annotation cost of large numbers of
fine-grained categories. This project shows that compelling classification
performance can be achieved on such categories even without labeled training
data. Given image and class embeddings, we learn a compatibility function such
that matching embeddings are assigned a higher score than mismatching ones;
zero-shot classification of an image proceeds by finding the label yielding the
highest joint compatibility score. We use state-of-the-art image features and
focus on different supervised attributes and unsupervised output embeddings
either derived from hierarchies or learned from unlabeled text corpora. We
establish a substantially improved state-of-the-art on the Animals with
Attributes and Caltech-UCSD Birds datasets. Most encouragingly, we demonstrate
that purely unsupervised output embeddings (learned from Wikipedia and improved
with fine-grained text) achieve compelling results, even outperforming the
previous supervised state-of-the-art. By combining different output embeddings,
we further improve results.Comment: @inproceedings {ARWLS15, title = {Evaluation of Output Embeddings for
Fine-Grained Image Classification}, booktitle = {IEEE Computer Vision and
Pattern Recognition}, year = {2015}, author = {Zeynep Akata and Scott Reed
and Daniel Walter and Honglak Lee and Bernt Schiele}
Ensuring the discoverability of digital images for social work education : an online tagging survey to test controlled vocabularies
The digital age has transformed access to all kinds of educational content not only in text-based format but also digital images and other media. As learning technologists and librarians begin to organise these new media into digital collections for educational purposes, older problems associated with cataloguing and classifying non-text media have re-emerged. At the heart of this issue is the problem of describing complex and highly subjective images in a reliable and consistent manner. This paper reports on the findings of research designed to test the suitability of two controlled vocabularies to index and thereby improve the discoverability of images stored in the Learning Exchange, a repository for social work education and research. An online survey asked respondents to "tag", a series of images and responses were mapped against the two controlled vocabularies. Findings showed that a large proportion of user generated tags could be mapped to the controlled vocabulary terms (or their equivalents). The implications of these findings for indexing and discovering content are discussed in the context of a wider review of the literature on "folksonomies" (or user tagging) versus taxonomies and controlled vocabularies
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