1,110 research outputs found
Convolutional Color Constancy
Color constancy is the problem of inferring the color of the light that
illuminated a scene, usually so that the illumination color can be removed.
Because this problem is underconstrained, it is often solved by modeling the
statistical regularities of the colors of natural objects and illumination. In
contrast, in this paper we reformulate the problem of color constancy as a 2D
spatial localization task in a log-chrominance space, thereby allowing us to
apply techniques from object detection and structured prediction to the color
constancy problem. By directly learning how to discriminate between correctly
white-balanced images and poorly white-balanced images, our model is able to
improve performance on standard benchmarks by nearly 40%
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
Discriminatively Trained Latent Ordinal Model for Video Classification
We study the problem of video classification for facial analysis and human
action recognition. We propose a novel weakly supervised learning method that
models the video as a sequence of automatically mined, discriminative
sub-events (eg. onset and offset phase for "smile", running and jumping for
"highjump"). The proposed model is inspired by the recent works on Multiple
Instance Learning and latent SVM/HCRF -- it extends such frameworks to model
the ordinal aspect in the videos, approximately. We obtain consistent
improvements over relevant competitive baselines on four challenging and
publicly available video based facial analysis datasets for prediction of
expression, clinical pain and intent in dyadic conversations and on three
challenging human action datasets. We also validate the method with qualitative
results and show that they largely support the intuitions behind the method.Comment: Paper accepted in IEEE TPAMI. arXiv admin note: substantial text
overlap with arXiv:1604.0150
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