12,270 research outputs found
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
Minimizing Supervision in Multi-label Categorization
Multiple categories of objects are present in most images. Treating this as a
multi-class classification is not justified. We treat this as a multi-label
classification problem. In this paper, we further aim to minimize the
supervision required for providing supervision in multi-label classification.
Specifically, we investigate an effective class of approaches that associate a
weak localization with each category either in terms of the bounding box or
segmentation mask. Doing so improves the accuracy of multi-label
categorization. The approach we adopt is one of active learning, i.e.,
incrementally selecting a set of samples that need supervision based on the
current model, obtaining supervision for these samples, retraining the model
with the additional set of supervised samples and proceeding again to select
the next set of samples. A crucial concern is the choice of the set of samples.
In doing so, we provide a novel insight, and no specific measure succeeds in
obtaining a consistently improved selection criterion. We, therefore, provide a
selection criterion that consistently improves the overall baseline criterion
by choosing the top k set of samples for a varied set of criteria. Using this
criterion, we are able to show that we can retain more than 98% of the fully
supervised performance with just 20% of samples (and more than 96% using 10%)
of the dataset on PASCAL VOC 2007 and 2012. Also, our proposed approach
consistently outperforms all other baseline metrics for all benchmark datasets
and model combinations.Comment: Accepted in CVPR-W 202
BatchRank: A Novel Batch Mode Active Learning Framework for Hierarchical Classification
Active learning algorithms automatically identify the salient
and exemplar instances from large amounts of unlabeled
data and thus reduce human annotation effort in inducing
a classification model. More recently, Batch Mode Active
Learning (BMAL) techniques have been proposed, where a
batch of data samples is selected simultaneously from an un-
labeled set. Most active learning algorithms assume a
at
label space, that is, they consider the class labels to be in-
dependent. However, in many applications, the set of class
labels are organized in a hierarchical tree structure, with
the leaf nodes as outputs and the internal nodes as clusters
of outputs at multiple levels of granularity. In this paper,
we propose a novel BMAL algorithm (BatchRank) for hi-
erarchical classification. The sample selection is posed as
an NP-hard integer quadratic programming problem and a
convex relaxation (based on linear programming) is derived,
whose solution is further improved by an iterative truncated
power method. Finally, a deterministic bound is established
on the quality of the solution. Our empirical results on sev-
eral challenging, real-world datasets from multiple domains,
corroborate the potential of the proposed framework for real-
world hierarchical classification applications
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