361 research outputs found
Co-Regularized Deep Representations for Video Summarization
Compact keyframe-based video summaries are a popular way of generating
viewership on video sharing platforms. Yet, creating relevant and compelling
summaries for arbitrarily long videos with a small number of keyframes is a
challenging task. We propose a comprehensive keyframe-based summarization
framework combining deep convolutional neural networks and restricted Boltzmann
machines. An original co-regularization scheme is used to discover meaningful
subject-scene associations. The resulting multimodal representations are then
used to select highly-relevant keyframes. A comprehensive user study is
conducted comparing our proposed method to a variety of schemes, including the
summarization currently in use by one of the most popular video sharing
websites. The results show that our method consistently outperforms the
baseline schemes for any given amount of keyframes both in terms of
attractiveness and informativeness. The lead is even more significant for
smaller summaries.Comment: Video summarization, deep convolutional neural networks,
co-regularized restricted Boltzmann machine
Group Invariant Deep Representations for Image Instance Retrieval
Most image instance retrieval pipelines are based on comparison of vectors
known as global image descriptors between a query image and the database
images. Due to their success in large scale image classification,
representations extracted from Convolutional Neural Networks (CNN) are quickly
gaining ground on Fisher Vectors (FVs) as state-of-the-art global descriptors
for image instance retrieval. While CNN-based descriptors are generally
remarked for good retrieval performance at lower bitrates, they nevertheless
present a number of drawbacks including the lack of robustness to common object
transformations such as rotations compared with their interest point based FV
counterparts.
In this paper, we propose a method for computing invariant global descriptors
from CNNs. Our method implements a recently proposed mathematical theory for
invariance in a sensory cortex modeled as a feedforward neural network. The
resulting global descriptors can be made invariant to multiple arbitrary
transformation groups while retaining good discriminativeness.
Based on a thorough empirical evaluation using several publicly available
datasets, we show that our method is able to significantly and consistently
improve retrieval results every time a new type of invariance is incorporated.
We also show that our method which has few parameters is not prone to
overfitting: improvements generalize well across datasets with different
properties with regard to invariances. Finally, we show that our descriptors
are able to compare favourably to other state-of-the-art compact descriptors in
similar bitranges, exceeding the highest retrieval results reported in the
literature on some datasets. A dedicated dimensionality reduction step
--quantization or hashing-- may be able to further improve the competitiveness
of the descriptors
Simultaneous Parameters Identification and State Estimation based on Unknown Input Observer for a class of LPV Systems
International audienceA novel unknown input observer structure for parameters and state estimation is proposed to enhance the performance of the estimator. In this paper, we suggest how a failed matching condition in a nonlinear unknown input observer can be recovered by using time delayed measurement to solve the inversing problem. Based on delayed outputs, an augmented system is constructed from which the parameters of the model and the system states can be simultaneously estimated. The augmented nonlinear model is transformed into a Takagi Sugeno (TS) form. Sufficient conditions for the existence of the estimator are given in terms of linear matrix inequalities (LMIs). Using the obtained information on the unknown input observer, unknown parameters are identified. Finally, the feasibility and the effectiveness of the suggested approach is demonstrated on examples
Avant-propos
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