122,853 research outputs found
Enhanced image annotations based on spatial information extraction and ontologies
Current research on image annotation often represents images in terms of labelled regions or objects, but pays little attention to the spatial positions or relationships between those regions or objects. To be effective, general purpose image retrieval systems require images with comprehensive annotations describing fully the content of the image. Much research is being done on automatic image annotation schemes but few authors address the issue of spatial annotations directly. This paper begins with a brief analysis of real picture queries to librarians showing how spatial terms are used to formulate queries. The paper is then concerned with the development of an enhanced automatic image annotation system, which extracts spatial information about objects in the image. The approach uses region boundaries and region labels to generate annotations describing absolute object positions and also relative positions between pairs of objects. A domain ontology and spatial information ontology are also used to extract more complex information about the relative closeness of objects to the viewer
Generating Labels for Regression of Subjective Constructs using Triplet Embeddings
Human annotations serve an important role in computational models where the
target constructs under study are hidden, such as dimensions of affect. This is
especially relevant in machine learning, where subjective labels derived from
related observable signals (e.g., audio, video, text) are needed to support
model training and testing. Current research trends focus on correcting
artifacts and biases introduced by annotators during the annotation process
while fusing them into a single annotation. In this work, we propose a novel
annotation approach using triplet embeddings. By lifting the absolute
annotation process to relative annotations where the annotator compares
individual target constructs in triplets, we leverage the accuracy of
comparisons over absolute ratings by human annotators. We then build a
1-dimensional embedding in Euclidean space that is indexed in time and serves
as a label for regression. In this setting, the annotation fusion occurs
naturally as a union of sets of sampled triplet comparisons among different
annotators. We show that by using our proposed sampling method to find an
embedding, we are able to accurately represent synthetic hidden constructs in
time under noisy sampling conditions. We further validate this approach using
human annotations collected from Mechanical Turk and show that we can recover
the underlying structure of the hidden construct up to bias and scaling
factors.Comment: 9 pages, 5 figures, accepted journal pape
Analyzing structural characteristics of object category representations from their semantic-part distributions
Studies from neuroscience show that part-mapping computations are employed by
human visual system in the process of object recognition. In this work, we
present an approach for analyzing semantic-part characteristics of object
category representations. For our experiments, we use category-epitome, a
recently proposed sketch-based spatial representation for objects. To enable
part-importance analysis, we first obtain semantic-part annotations of
hand-drawn sketches originally used to construct the corresponding epitomes. We
then examine the extent to which the semantic-parts are present in the epitomes
of a category and visualize the relative importance of parts as a word cloud.
Finally, we show how such word cloud visualizations provide an intuitive
understanding of category-level structural trends that exist in the
category-epitome object representations
Benchmarking gene ontology function predictions using negative annotations.
With the ever-increasing number and diversity of sequenced species, the challenge to characterize genes with functional information is even more important. In most species, this characterization almost entirely relies on automated electronic methods. As such, it is critical to benchmark the various methods. The Critical Assessment of protein Function Annotation algorithms (CAFA) series of community experiments provide the most comprehensive benchmark, with a time-delayed analysis leveraging newly curated experimentally supported annotations. However, the definition of a false positive in CAFA has not fully accounted for the open world assumption (OWA), leading to a systematic underestimation of precision. The main reason for this limitation is the relative paucity of negative experimental annotations.
This article introduces a new, OWA-compliant, benchmark based on a balanced test set of positive and negative annotations. The negative annotations are derived from expert-curated annotations of protein families on phylogenetic trees. This approach results in a large increase in the average information content of negative annotations. The benchmark has been tested using the naïve and BLAST baseline methods, as well as two orthology-based methods. This new benchmark could complement existing ones in future CAFA experiments.
All data, as well as code used for analysis, is available from https://lab.dessimoz.org/20_not.
Supplementary data are available at Bioinformatics online
Understanding Image Virality
Virality of online content on social networking websites is an important but
esoteric phenomenon often studied in fields like marketing, psychology and data
mining. In this paper we study viral images from a computer vision perspective.
We introduce three new image datasets from Reddit, and define a virality score
using Reddit metadata. We train classifiers with state-of-the-art image
features to predict virality of individual images, relative virality in pairs
of images, and the dominant topic of a viral image. We also compare machine
performance to human performance on these tasks. We find that computers perform
poorly with low level features, and high level information is critical for
predicting virality. We encode semantic information through relative
attributes. We identify the 5 key visual attributes that correlate with
virality. We create an attribute-based characterization of images that can
predict relative virality with 68.10% accuracy (SVM+Deep Relative Attributes)
-- better than humans at 60.12%. Finally, we study how human prediction of
image virality varies with different `contexts' in which the images are viewed,
such as the influence of neighbouring images, images recently viewed, as well
as the image title or caption. This work is a first step in understanding the
complex but important phenomenon of image virality. Our datasets and
annotations will be made publicly available.Comment: Pre-print, IEEE Conference on Computer Vision and Pattern Recognition
(CVPR), 201
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