9,843 research outputs found
A Review of Codebook Models in Patch-Based Visual Object Recognition
The codebook model-based approach, while ignoring any structural aspect in vision, nonetheless provides state-of-the-art performances on current datasets. The key role of a visual codebook is to provide a way to map the low-level features into a fixed-length vector in histogram space to which standard classifiers can be directly applied. The discriminative power of such a visual codebook determines the quality of the codebook model, whereas the size of the codebook controls the complexity of the model. Thus, the construction of a codebook is an important step which is usually done by cluster analysis. However, clustering is a process that retains regions of high density in a distribution and it follows that the resulting codebook need not have discriminant properties. This is also recognised as a computational bottleneck of such systems. In our recent work, we proposed a resource-allocating codebook, to constructing a discriminant codebook in a one-pass design procedure that slightly outperforms more traditional approaches at drastically reduced computing times. In this review we survey several approaches that have been proposed over the last decade with their use of feature detectors, descriptors, codebook construction schemes, choice of classifiers in recognising objects, and datasets that were used in evaluating the proposed methods
Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Ugly
Due to the importance of zero-shot learning, i.e. classifying images where
there is a lack of labeled training data, the number of proposed approaches has
recently increased steadily. We argue that it is time to take a step back and
to analyze the status quo of the area. The purpose of this paper is three-fold.
First, given the fact that there is no agreed upon zero-shot learning
benchmark, we first define a new benchmark by unifying both the evaluation
protocols and data splits of publicly available datasets used for this task.
This is an important contribution as published results are often not comparable
and sometimes even flawed due to, e.g. pre-training on zero-shot test classes.
Moreover, we propose a new zero-shot learning dataset, the Animals with
Attributes 2 (AWA2) dataset which we make publicly available both in terms of
image features and the images themselves. Second, we compare and analyze a
significant number of the state-of-the-art methods in depth, both in the
classic zero-shot setting but also in the more realistic generalized zero-shot
setting. Finally, we discuss in detail the limitations of the current status of
the area which can be taken as a basis for advancing it.Comment: Accepted by TPAMI in July, 2018. We introduce Proposed Split Version
2.0 (Please download it from our project webpage). arXiv admin note:
substantial text overlap with arXiv:1703.0439
One-Class Classification: Taxonomy of Study and Review of Techniques
One-class classification (OCC) algorithms aim to build classification models
when the negative class is either absent, poorly sampled or not well defined.
This unique situation constrains the learning of efficient classifiers by
defining class boundary just with the knowledge of positive class. The OCC
problem has been considered and applied under many research themes, such as
outlier/novelty detection and concept learning. In this paper we present a
unified view of the general problem of OCC by presenting a taxonomy of study
for OCC problems, which is based on the availability of training data,
algorithms used and the application domains applied. We further delve into each
of the categories of the proposed taxonomy and present a comprehensive
literature review of the OCC algorithms, techniques and methodologies with a
focus on their significance, limitations and applications. We conclude our
paper by discussing some open research problems in the field of OCC and present
our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure
- …