67,688 research outputs found
Searching and organizing images across languages
With the continual growth of users on the Web
from a wide range of countries, supporting
such users in their search of cultural heritage
collections will grow in importance. In the
next few years, the growth areas of Internet
users will come from the Indian sub-continent
and China. Consequently, if holders of cultural
heritage collections wish their content to be
viewable by the full range of users coming to
the Internet, the range of languages that they
need to support will have to grow. This paper
will present recent work conducted at the
University of Sheffield (and now being
implemented in BRICKS) on how to use
automatic translation to provide search and
organisation facilities for a historical image
search engine. The system allows users to
search for images in seven different languages,
providing means for the user to examine
translated image captions and browse retrieved
images organised by categories written in their
native language
User experiments with the Eurovision cross-language image retrieval system
In this paper we present Eurovision, a text-based system for cross-language (CL) image retrieval.
The system is evaluated by multilingual users for two search tasks with the system configured in
English and five other languages. To our knowledge this is the first published set of user
experiments for CL image retrieval. We show that: (1) it is possible to create a usable multilingual
search engine using little knowledge of any language other than English, (2) categorizing images
assists the user's search, and (3) there are differences in the way users search between the proposed
search tasks. Based on the two search tasks and user feedback, we describe important aspects of
any CL image retrieval system
Dublin City University at CLEF 2006: Experiments for the ImageCLEF Photo Collection Standard Ad Hoc Task
We provide a technical description of our submission to the CLEF 2006 Cross Language Image Retrieval(ImageCLEF) Photo Collection Standard Ad Hoc task. We performed monolingual and cross language retrieval of photo images using photo annotations with and without feedback, and also a combined visual and text retrieval approach. Topics are translated into English using the Babelfish online machine translation
system. Our text runs used the BM25 algorithm, while our visual approach used simple low-level features with matching based on the Jeffrey Divergence measure. Our results consistently indicate that the fusion of text and visual features is best for this task, and that performing feedback for text consistently improves on the baseline
non-feedback BM25 text runs for all language pairs
SMAN : Stacked Multi-Modal Attention Network for cross-modal image-text retrieval
This article focuses on tackling the task of the cross-modal image-text retrieval which has been an interdisciplinary topic in both computer vision and natural language processing communities. Existing global representation alignment-based methods fail to pinpoint the semantically meaningful portion of images and texts, while the local representation alignment schemes suffer from the huge computational burden for aggregating the similarity of visual fragments and textual words exhaustively. In this article, we propose a stacked multimodal attention network (SMAN) that makes use of the stacked multimodal attention mechanism to exploit the fine-grained interdependencies between image and text, thereby mapping the aggregation of attentive fragments into a common space for measuring cross-modal similarity. Specifically, we sequentially employ intramodal information and multimodal information as guidance to perform multiple-step attention reasoning so that the fine-grained correlation between image and text can be modeled. As a consequence, we are capable of discovering the semantically meaningful visual regions or words in a sentence which contributes to measuring the cross-modal similarity in a more precise manner. Moreover, we present a novel bidirectional ranking loss that enforces the distance among pairwise multimodal instances to be closer. Doing so allows us to make full use of pairwise supervised information to preserve the manifold structure of heterogeneous pairwise data. Extensive experiments on two benchmark datasets demonstrate that our SMAN consistently yields competitive performance compared to state-of-the-art methods
Evaluating Text-to-Image Matching using Binary Image Selection (BISON)
Providing systems the ability to relate linguistic and visual content is one
of the hallmarks of computer vision. Tasks such as text-based image retrieval
and image captioning were designed to test this ability but come with
evaluation measures that have a high variance or are difficult to interpret. We
study an alternative task for systems that match text and images: given a text
query, the system is asked to select the image that best matches the query from
a pair of semantically similar images. The system's accuracy on this Binary
Image SelectiON (BISON) task is interpretable, eliminates the reliability
problems of retrieval evaluations, and focuses on the system's ability to
understand fine-grained visual structure. We gather a BISON dataset that
complements the COCO dataset and use it to evaluate modern text-based image
retrieval and image captioning systems. Our results provide novel insights into
the performance of these systems. The COCO-BISON dataset and corresponding
evaluation code are publicly available from \url{http://hexianghu.com/bison/}
Concept hierarchy across languages in text-based image retrieval: a user evaluation
The University of Sheffield participated in Interactive ImageCLEF 2005 with a comparative user
evaluation of two interfaces: one displaying search results as a list, the other organizing retrieved images into
a hierarchy of concepts displayed on the interface as an interactive menu. Data was analysed with respect to
effectiveness (number of images retrieved), efficiency (time needed) and user satisfaction (opinions from
questionnaires). Effectiveness and efficiency were calculated at both 5 minutes (CLEF condition) and at final
time. The list was marginally more effective than the menu at 5 minutes (no statistical significance) but the
two were equal at final time showing the menu needs more time to be effectively used. The list was more efficient
at both 5 minutes and final time, although the difference was not statistically significant. Users preferred
the menu (75% vs. 25% for the list) indicating it to be an interesting and engaging feature. An inspection
of the logs showed that 11% of effective terms (i.e. no stop-words, single terms) were not translated and
that another 5% were ill translations. Some of those terms were used by all participants and were fundamental
for some of the tasks. Non translated and ill translated terms negatively affected the search, hierarchy generation
and, results display. More work has to be carried out to test the system under different setting, e.g. using
a dictionary instead of MT that appears to be ineffective in translating users’ queries that rarely are
grammatically correct. The evaluation also indicated directions for a new interface design that allows the user
to check query translation (in both input and output) and that incorporates visual content image retrieval to
improve result organization
Creating a test collection to evaluate diversity in image retrieval
This paper describes the adaptation of an existing test collection
for image retrieval to enable diversity in the results set to be
measured. Previous research has shown that a more diverse set of
results often satisfies the needs of more users better than standard
document rankings. To enable diversity to be quantified, it is
necessary to classify images relevant to a given theme to one or
more sub-topics or clusters. We describe the challenges in
building (as far as we are aware) the first test collection for
evaluating diversity in image retrieval. This includes selecting
appropriate topics, creating sub-topics, and quantifying the overall
effectiveness of a retrieval system. A total of 39 topics were
augmented for cluster-based relevance and we also provide an
initial analysis of assessor agreement for grouping relevant
images into sub-topics or clusters
Semantic bottleneck for computer vision tasks
This paper introduces a novel method for the representation of images that is
semantic by nature, addressing the question of computation intelligibility in
computer vision tasks. More specifically, our proposition is to introduce what
we call a semantic bottleneck in the processing pipeline, which is a crossing
point in which the representation of the image is entirely expressed with
natural language , while retaining the efficiency of numerical representations.
We show that our approach is able to generate semantic representations that
give state-of-the-art results on semantic content-based image retrieval and
also perform very well on image classification tasks. Intelligibility is
evaluated through user centered experiments for failure detection
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