13,270 research outputs found
Scale Selective Extended Local Binary Pattern for Texture Classification
In this paper, we propose a new texture descriptor, scale selective extended
local binary pattern (SSELBP), to characterize texture images with scale
variations. We first utilize multi-scale extended local binary patterns (ELBP)
with rotation-invariant and uniform mappings to capture robust local micro- and
macro-features. Then, we build a scale space using Gaussian filters and
calculate the histogram of multi-scale ELBPs for the image at each scale.
Finally, we select the maximum values from the corresponding bins of
multi-scale ELBP histograms at different scales as scale-invariant features. A
comprehensive evaluation on public texture databases (KTH-TIPS and UMD) shows
that the proposed SSELBP has high accuracy comparable to state-of-the-art
texture descriptors on gray-scale-, rotation-, and scale-invariant texture
classification but uses only one-third of the feature dimension.Comment: IEEE International Conference on Acoustics, Speech and Signal
Processing (ICASSP), 201
Robust Adaptive Median Binary Pattern for noisy texture classification and retrieval
Texture is an important cue for different computer vision tasks and
applications. Local Binary Pattern (LBP) is considered one of the best yet
efficient texture descriptors. However, LBP has some notable limitations,
mostly the sensitivity to noise. In this paper, we address these criteria by
introducing a novel texture descriptor, Robust Adaptive Median Binary Pattern
(RAMBP). RAMBP based on classification process of noisy pixels, adaptive
analysis window, scale analysis and image regions median comparison. The
proposed method handles images with high noisy textures, and increases the
discriminative properties by capturing microstructure and macrostructure
texture information. The proposed method has been evaluated on popular texture
datasets for classification and retrieval tasks, and under different high noise
conditions. Without any train or prior knowledge of noise type, RAMBP achieved
the best classification compared to state-of-the-art techniques. It scored more
than under impulse noise densities, more than under
Gaussian noised textures with standard deviation , and more than
under Gaussian blurred textures with standard deviation .
The proposed method yielded competitive results and high performance as one of
the best descriptors in noise-free texture classification. Furthermore, RAMBP
showed also high performance for the problem of noisy texture retrieval
providing high scores of recall and precision measures for textures with high
levels of noise
Dynamic texture recognition using time-causal and time-recursive spatio-temporal receptive fields
This work presents a first evaluation of using spatio-temporal receptive
fields from a recently proposed time-causal spatio-temporal scale-space
framework as primitives for video analysis. We propose a new family of video
descriptors based on regional statistics of spatio-temporal receptive field
responses and evaluate this approach on the problem of dynamic texture
recognition. Our approach generalises a previously used method, based on joint
histograms of receptive field responses, from the spatial to the
spatio-temporal domain and from object recognition to dynamic texture
recognition. The time-recursive formulation enables computationally efficient
time-causal recognition. The experimental evaluation demonstrates competitive
performance compared to state-of-the-art. Especially, it is shown that binary
versions of our dynamic texture descriptors achieve improved performance
compared to a large range of similar methods using different primitives either
handcrafted or learned from data. Further, our qualitative and quantitative
investigation into parameter choices and the use of different sets of receptive
fields highlights the robustness and flexibility of our approach. Together,
these results support the descriptive power of this family of time-causal
spatio-temporal receptive fields, validate our approach for dynamic texture
recognition and point towards the possibility of designing a range of video
analysis methods based on these new time-causal spatio-temporal primitives.Comment: 29 pages, 16 figure
When Face Recognition Meets with Deep Learning: an Evaluation of Convolutional Neural Networks for Face Recognition
Deep learning, in particular Convolutional Neural Network (CNN), has achieved
promising results in face recognition recently. However, it remains an open
question: why CNNs work well and how to design a 'good' architecture. The
existing works tend to focus on reporting CNN architectures that work well for
face recognition rather than investigate the reason. In this work, we conduct
an extensive evaluation of CNN-based face recognition systems (CNN-FRS) on a
common ground to make our work easily reproducible. Specifically, we use public
database LFW (Labeled Faces in the Wild) to train CNNs, unlike most existing
CNNs trained on private databases. We propose three CNN architectures which are
the first reported architectures trained using LFW data. This paper
quantitatively compares the architectures of CNNs and evaluate the effect of
different implementation choices. We identify several useful properties of
CNN-FRS. For instance, the dimensionality of the learned features can be
significantly reduced without adverse effect on face recognition accuracy. In
addition, traditional metric learning method exploiting CNN-learned features is
evaluated. Experiments show two crucial factors to good CNN-FRS performance are
the fusion of multiple CNNs and metric learning. To make our work reproducible,
source code and models will be made publicly available.Comment: 7 pages, 4 figures, 7 table
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