937 research outputs found
Image classification by visual bag-of-words refinement and reduction
This paper presents a new framework for visual bag-of-words (BOW) refinement
and reduction to overcome the drawbacks associated with the visual BOW model
which has been widely used for image classification. Although very influential
in the literature, the traditional visual BOW model has two distinct drawbacks.
Firstly, for efficiency purposes, the visual vocabulary is commonly constructed
by directly clustering the low-level visual feature vectors extracted from
local keypoints, without considering the high-level semantics of images. That
is, the visual BOW model still suffers from the semantic gap, and thus may lead
to significant performance degradation in more challenging tasks (e.g. social
image classification). Secondly, typically thousands of visual words are
generated to obtain better performance on a relatively large image dataset. Due
to such large vocabulary size, the subsequent image classification may take
sheer amount of time. To overcome the first drawback, we develop a graph-based
method for visual BOW refinement by exploiting the tags (easy to access
although noisy) of social images. More notably, for efficient image
classification, we further reduce the refined visual BOW model to a much
smaller size through semantic spectral clustering. Extensive experimental
results show the promising performance of the proposed framework for visual BOW
refinement and reduction
Enriching ontological user profiles with tagging history for multi-domain recommendations
Many advanced recommendation frameworks employ ontologies of various complexities to model individuals and items, providing a mechanism for the expression of user interests and the representation of item attributes. As a result, complex matching techniques can be applied to support individuals in the discovery of items according to explicit and implicit user preferences. Recently, the rapid adoption of Web2.0, and the proliferation of social networking sites, has resulted in more and more users providing an increasing amount of information about themselves that could be exploited for recommendation purposes. However, the unification of personal information with ontologies using the contemporary knowledge representation methods often associated with Web2.0 applications, such as community tagging, is a non-trivial task. In this paper, we propose a method for the unification of tags with ontologies by grounding tags to a shared representation in the form of Wordnet and Wikipedia. We incorporate individuals' tagging history into their ontological profiles by matching tags with ontology concepts. This approach is preliminary evaluated by extending an existing news recommendation system with user tagging histories harvested from popular social networking sites
Mind the Gap: Subspace based Hierarchical Domain Adaptation
Domain adaptation techniques aim at adapting a classifier learnt on a source
domain to work on the target domain. Exploiting the subspaces spanned by
features of the source and target domains respectively is one approach that has
been investigated towards solving this problem. These techniques normally
assume the existence of a single subspace for the entire source / target
domain. In this work, we consider the hierarchical organization of the data and
consider multiple subspaces for the source and target domain based on the
hierarchy. We evaluate different subspace based domain adaptation techniques
under this setting and observe that using different subspaces based on the
hierarchy yields consistent improvement over a non-hierarchical baselineComment: 4 pages in Second Workshop on Transfer and Multi-Task Learning:
Theory meets Practice in NIPS 201
Review of the state of the art: discovering and associating semantics to tags in folksonomies
This paper describes and compares the most relevant approaches for associating tags with semantics in order to make explicit the meaning of those tags. We identify a common set of steps that are usually considered across all these approaches and frame our descriptions according to them, providing a unified view of how each approach tackles the different problems that appear during the semantic association process. Furthermore, we provide some recommendations on (a) how and when to use each of the approaches according to the characteristics of the data source, and (b) how to improve results by leveraging the strengths of the different approaches
Visualizing and Interacting with Concept Hierarchies
Concept Hierarchies and Formal Concept Analysis are theoretically well
grounded and largely experimented methods. They rely on line diagrams called
Galois lattices for visualizing and analysing object-attribute sets. Galois
lattices are visually seducing and conceptually rich for experts. However they
present important drawbacks due to their concept oriented overall structure:
analysing what they show is difficult for non experts, navigation is
cumbersome, interaction is poor, and scalability is a deep bottleneck for
visual interpretation even for experts. In this paper we introduce semantic
probes as a means to overcome many of these problems and extend usability and
application possibilities of traditional FCA visualization methods. Semantic
probes are visual user centred objects which extract and organize reduced
Galois sub-hierarchies. They are simpler, clearer, and they provide a better
navigation support through a rich set of interaction possibilities. Since probe
driven sub-hierarchies are limited to users focus, scalability is under control
and interpretation is facilitated. After some successful experiments, several
applications are being developed with the remaining problem of finding a
compromise between simplicity and conceptual expressivity
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