21,063 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
A fine-grained approach to scene text script identification
This paper focuses on the problem of script identification in unconstrained
scenarios. Script identification is an important prerequisite to recognition,
and an indispensable condition for automatic text understanding systems
designed for multi-language environments. Although widely studied for document
images and handwritten documents, it remains an almost unexplored territory for
scene text images.
We detail a novel method for script identification in natural images that
combines convolutional features and the Naive-Bayes Nearest Neighbor
classifier. The proposed framework efficiently exploits the discriminative
power of small stroke-parts, in a fine-grained classification framework.
In addition, we propose a new public benchmark dataset for the evaluation of
joint text detection and script identification in natural scenes. Experiments
done in this new dataset demonstrate that the proposed method yields state of
the art results, while it generalizes well to different datasets and variable
number of scripts. The evidence provided shows that multi-lingual scene text
recognition in the wild is a viable proposition. Source code of the proposed
method is made available online
Efficient Asymmetric Co-Tracking using Uncertainty Sampling
Adaptive tracking-by-detection approaches are popular for tracking arbitrary
objects. They treat the tracking problem as a classification task and use
online learning techniques to update the object model. However, these
approaches are heavily invested in the efficiency and effectiveness of their
detectors. Evaluating a massive number of samples for each frame (e.g.,
obtained by a sliding window) forces the detector to trade the accuracy in
favor of speed. Furthermore, misclassification of borderline samples in the
detector introduce accumulating errors in tracking. In this study, we propose a
co-tracking based on the efficient cooperation of two detectors: a rapid
adaptive exemplar-based detector and another more sophisticated but slower
detector with a long-term memory. The sampling labeling and co-learning of the
detectors are conducted by an uncertainty sampling unit, which improves the
speed and accuracy of the system. We also introduce a budgeting mechanism which
prevents the unbounded growth in the number of examples in the first detector
to maintain its rapid response. Experiments demonstrate the efficiency and
effectiveness of the proposed tracker against its baselines and its superior
performance against state-of-the-art trackers on various benchmark videos.Comment: Submitted to IEEE ICSIPA'201
Generative and Discriminative Text Classification with Recurrent Neural Networks
We empirically characterize the performance of discriminative and generative
LSTM models for text classification. We find that although RNN-based generative
models are more powerful than their bag-of-words ancestors (e.g., they account
for conditional dependencies across words in a document), they have higher
asymptotic error rates than discriminatively trained RNN models. However we
also find that generative models approach their asymptotic error rate more
rapidly than their discriminative counterparts---the same pattern that Ng &
Jordan (2001) proved holds for linear classification models that make more
naive conditional independence assumptions. Building on this finding, we
hypothesize that RNN-based generative classification models will be more robust
to shifts in the data distribution. This hypothesis is confirmed in a series of
experiments in zero-shot and continual learning settings that show that
generative models substantially outperform discriminative models
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