3,301 research outputs found
A study into annotation ranking metrics in geo-tagged image corpora
Community contributed datasets are becoming increasingly common in automated image annotation systems. One important issue with community image data is that there is no guarantee that the associated metadata is relevant. A method is required that can accurately rank the semantic relevance of community annotations. This should enable the extracting of relevant subsets from potentially noisy collections of these annotations. Having relevant, non heterogeneous tags assigned to images should improve community image retrieval systems, such as Flickr, which are based on text retrieval methods. In the literature, the current state of the art approach to ranking the semantic relevance of Flickr tags is based on the widely used tf-idf metric. In the case of datasets containing landmark images, however, this metric is inefficient due to the high frequency of common landmark tags within the data set and can be improved upon. In this paper, we present a landmark recognition framework, that provides end-to-end automated recognition and annotation. In our study into automated annotation, we evaluate 5 alternate approaches to tf-idf
to rank tag relevance in community contributed landmark image corpora. We carry out a thorough evaluation of each of these ranking metrics and results of this evaluation demonstrate that four of these proposed techniques outperform the current commonly-used tf-idf approach for this task
A Data-Driven Approach for Tag Refinement and Localization in Web Videos
Tagging of visual content is becoming more and more widespread as web-based
services and social networks have popularized tagging functionalities among
their users. These user-generated tags are used to ease browsing and
exploration of media collections, e.g. using tag clouds, or to retrieve
multimedia content. However, not all media are equally tagged by users. Using
the current systems is easy to tag a single photo, and even tagging a part of a
photo, like a face, has become common in sites like Flickr and Facebook. On the
other hand, tagging a video sequence is more complicated and time consuming, so
that users just tag the overall content of a video. In this paper we present a
method for automatic video annotation that increases the number of tags
originally provided by users, and localizes them temporally, associating tags
to keyframes. Our approach exploits collective knowledge embedded in
user-generated tags and web sources, and visual similarity of keyframes and
images uploaded to social sites like YouTube and Flickr, as well as web sources
like Google and Bing. Given a keyframe, our method is able to select on the fly
from these visual sources the training exemplars that should be the most
relevant for this test sample, and proceeds to transfer labels across similar
images. Compared to existing video tagging approaches that require training
classifiers for each tag, our system has few parameters, is easy to implement
and can deal with an open vocabulary scenario. We demonstrate the approach on
tag refinement and localization on DUT-WEBV, a large dataset of web videos, and
show state-of-the-art results.Comment: Preprint submitted to Computer Vision and Image Understanding (CVIU
Automatic Concept Discovery from Parallel Text and Visual Corpora
Humans connect language and vision to perceive the world. How to build a
similar connection for computers? One possible way is via visual concepts,
which are text terms that relate to visually discriminative entities. We
propose an automatic visual concept discovery algorithm using parallel text and
visual corpora; it filters text terms based on the visual discriminative power
of the associated images, and groups them into concepts using visual and
semantic similarities. We illustrate the applications of the discovered
concepts using bidirectional image and sentence retrieval task and image
tagging task, and show that the discovered concepts not only outperform several
large sets of manually selected concepts significantly, but also achieves the
state-of-the-art performance in the retrieval task.Comment: To appear in ICCV 201
Adaptive Tag Selection for Image Annotation
Not all tags are relevant to an image, and the number of relevant tags is
image-dependent. Although many methods have been proposed for image
auto-annotation, the question of how to determine the number of tags to be
selected per image remains open. The main challenge is that for a large tag
vocabulary, there is often a lack of ground truth data for acquiring optimal
cutoff thresholds per tag. In contrast to previous works that pre-specify the
number of tags to be selected, we propose in this paper adaptive tag selection.
The key insight is to divide the vocabulary into two disjoint subsets, namely a
seen set consisting of tags having ground truth available for optimizing their
thresholds and a novel set consisting of tags without any ground truth. Such a
division allows us to estimate how many tags shall be selected from the novel
set according to the tags that have been selected from the seen set. The
effectiveness of the proposed method is justified by our participation in the
ImageCLEF 2014 image annotation task. On a set of 2,065 test images with ground
truth available for 207 tags, the benchmark evaluation shows that compared to
the popular top- strategy which obtains an F-score of 0.122, adaptive tag
selection achieves a higher F-score of 0.223. Moreover, by treating the
underlying image annotation system as a black box, the new method can be used
as an easy plug-in to boost the performance of existing systems
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Semantic Concept Co-Occurrence Patterns for Image Annotation and Retrieval.
Describing visual image contents by semantic concepts is an effective and straightforward way to facilitate various high level applications. Inferring semantic concepts from low-level pictorial feature analysis is challenging due to the semantic gap problem, while manually labeling concepts is unwise because of a large number of images in both online and offline collections. In this paper, we present a novel approach to automatically generate intermediate image descriptors by exploiting concept co-occurrence patterns in the pre-labeled training set that renders it possible to depict complex scene images semantically. Our work is motivated by the fact that multiple concepts that frequently co-occur across images form patterns which could provide contextual cues for individual concept inference. We discover the co-occurrence patterns as hierarchical communities by graph modularity maximization in a network with nodes and edges representing concepts and co-occurrence relationships separately. A random walk process working on the inferred concept probabilities with the discovered co-occurrence patterns is applied to acquire the refined concept signature representation. Through experiments in automatic image annotation and semantic image retrieval on several challenging datasets, we demonstrate the effectiveness of the proposed concept co-occurrence patterns as well as the concept signature representation in comparison with state-of-the-art approaches
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