15,507 research outputs found
Exemplar Based Deep Discriminative and Shareable Feature Learning for Scene Image Classification
In order to encode the class correlation and class specific information in
image representation, we propose a new local feature learning approach named
Deep Discriminative and Shareable Feature Learning (DDSFL). DDSFL aims to
hierarchically learn feature transformation filter banks to transform raw pixel
image patches to features. The learned filter banks are expected to: (1) encode
common visual patterns of a flexible number of categories; (2) encode
discriminative information; and (3) hierarchically extract patterns at
different visual levels. Particularly, in each single layer of DDSFL, shareable
filters are jointly learned for classes which share the similar patterns.
Discriminative power of the filters is achieved by enforcing the features from
the same category to be close, while features from different categories to be
far away from each other. Furthermore, we also propose two exemplar selection
methods to iteratively select training data for more efficient and effective
learning. Based on the experimental results, DDSFL can achieve very promising
performance, and it also shows great complementary effect to the
state-of-the-art Caffe features.Comment: Pattern Recognition, Elsevier, 201
Information-Theoretic Active Learning for Content-Based Image Retrieval
We propose Information-Theoretic Active Learning (ITAL), a novel batch-mode
active learning method for binary classification, and apply it for acquiring
meaningful user feedback in the context of content-based image retrieval.
Instead of combining different heuristics such as uncertainty, diversity, or
density, our method is based on maximizing the mutual information between the
predicted relevance of the images and the expected user feedback regarding the
selected batch. We propose suitable approximations to this computationally
demanding problem and also integrate an explicit model of user behavior that
accounts for possible incorrect labels and unnameable instances. Furthermore,
our approach does not only take the structure of the data but also the expected
model output change caused by the user feedback into account. In contrast to
other methods, ITAL turns out to be highly flexible and provides
state-of-the-art performance across various datasets, such as MIRFLICKR and
ImageNet.Comment: GCPR 2018 paper (14 pages text + 2 pages references + 6 pages
appendix
Crowdsourcing in Computer Vision
Computer vision systems require large amounts of manually annotated data to
properly learn challenging visual concepts. Crowdsourcing platforms offer an
inexpensive method to capture human knowledge and understanding, for a vast
number of visual perception tasks. In this survey, we describe the types of
annotations computer vision researchers have collected using crowdsourcing, and
how they have ensured that this data is of high quality while annotation effort
is minimized. We begin by discussing data collection on both classic (e.g.,
object recognition) and recent (e.g., visual story-telling) vision tasks. We
then summarize key design decisions for creating effective data collection
interfaces and workflows, and present strategies for intelligently selecting
the most important data instances to annotate. Finally, we conclude with some
thoughts on the future of crowdsourcing in computer vision.Comment: A 69-page meta review of the field, Foundations and Trends in
Computer Graphics and Vision, 201
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