25,228 research outputs found
Recognizing faces in news photographs on the web
We propose a graph based method in order to recognize the faces that appear on the web using a small training set. First, relevant pictures of the desired people are collected by querying the name in a text based search engine in order to construct the data set. Then, detected faces in these photographs are represented using SIFT features extracted from facial features. The similarities of faces are represented in a graph which is then used in random walk with restart algorithm to provide links between faces. Those links are used for recognition by using two different methods. © 2009 IEEE
Complex Event Recognition from Images with Few Training Examples
We propose to leverage concept-level representations for complex event
recognition in photographs given limited training examples. We introduce a
novel framework to discover event concept attributes from the web and use that
to extract semantic features from images and classify them into social event
categories with few training examples. Discovered concepts include a variety of
objects, scenes, actions and event sub-types, leading to a discriminative and
compact representation for event images. Web images are obtained for each
discovered event concept and we use (pretrained) CNN features to train concept
classifiers. Extensive experiments on challenging event datasets demonstrate
that our proposed method outperforms several baselines using deep CNN features
directly in classifying images into events with limited training examples. We
also demonstrate that our method achieves the best overall accuracy on a
dataset with unseen event categories using a single training example.Comment: Accepted to Winter Applications of Computer Vision (WACV'17
Brand Logos More Prevalent In Recent News Sports Photos
The exposure is non-intrusive, serving as a backdrop to the sports action occurring at the arena. Because it cannot be separated from the action, this communication form is difficult to tune out perceptually
Can we ID from CCTV? Image quality in digital CCTV and face identification performance
CCTV is used for an increasing number Of purposes, and the new generation of digital systems can be tailored to serve a wide range of security requirements. However, configuration decisions are often made without considering specific task requirements, e.g. the video quality needed for reliable person identification. Our Study investigated the relationship between video quality and the ability of untrained viewers to identify faces from digital CCTV images. The task required 80 participants to identify 64 faces belonging to 4 different ethnicities. Participants compared face images taken from a high quality photographs and low quality CCTV stills, which were recorded at 4 different video quality bit rates (32, 52, 72 and 92 Kbps). We found that the number of correct identifications decreased by 12 (similar to 18%) as MPEG-4 quality decreased from 92 to 32 Kbps, and by 4 (similar to 6%) as Wavelet video quality decreased from 92 to 32 Kbps. To achieve reliable and effective face identification, we recommend that MPEG-4 CCTV systems should be used over Wavelet, and video quality should not be lowered below 52 Kbps during video compression. We discuss the practical implications of these results for security, and contribute a contextual methodology for assessing CCTV video quality
Interesting faces: A graph-based approach for finding people in news
Cataloged from PDF version of article.In this study, we propose a method for finding people in large news photograph and video collections.
Our method exploits the multi-modal nature of these data sets to recognize people and does not require
any supervisory input. It first uses the name of the person to populate an initial set of candidate faces.
From this set, which is likely to include the faces of other people, it selects the group of most similar
faces corresponding to the queried person in a variety of conditions. Our main contribution is to
transform the problem of recognizing the faces of the queried person in a set of candidate faces to the
problem of finding the highly connected sub-graph (the densest component) in a graph representing
the similarities of faces. We also propose a novel technique for finding the similarities of faces by
matching interest points extracted from the faces. The proposed method further allows the
classification of new faces without needing to re-build the graph. The experiments are performed on
two data sets: thousands of news photographs from Yahoo! news and over 200 news videos from
TRECVid2004. The results show that the proposed method provides significant improvements over textbased
methods.
(C) 2009 Elsevier Ltd. All rights reserve
Level Playing Field for Million Scale Face Recognition
Face recognition has the perception of a solved problem, however when tested
at the million-scale exhibits dramatic variation in accuracies across the
different algorithms. Are the algorithms very different? Is access to good/big
training data their secret weapon? Where should face recognition improve? To
address those questions, we created a benchmark, MF2, that requires all
algorithms to be trained on same data, and tested at the million scale. MF2 is
a public large-scale set with 672K identities and 4.7M photos created with the
goal to level playing field for large scale face recognition. We contrast our
results with findings from the other two large-scale benchmarks MegaFace
Challenge and MS-Celebs-1M where groups were allowed to train on any
private/public/big/small set. Some key discoveries: 1) algorithms, trained on
MF2, were able to achieve state of the art and comparable results to algorithms
trained on massive private sets, 2) some outperformed themselves once trained
on MF2, 3) invariance to aging suffers from low accuracies as in MegaFace,
identifying the need for larger age variations possibly within identities or
adjustment of algorithms in future testings
Cultural Diffusion and Trends in Facebook Photographs
Online social media is a social vehicle in which people share various moments
of their lives with their friends, such as playing sports, cooking dinner or
just taking a selfie for fun, via visual means, that is, photographs. Our study
takes a closer look at the popular visual concepts illustrating various
cultural lifestyles from aggregated, de-identified photographs. We perform
analysis both at macroscopic and microscopic levels, to gain novel insights
about global and local visual trends as well as the dynamics of interpersonal
cultural exchange and diffusion among Facebook friends. We processed images by
automatically classifying the visual content by a convolutional neural network
(CNN). Through various statistical tests, we find that socially tied
individuals more likely post images showing similar cultural lifestyles. To
further identify the main cause of the observed social correlation, we use the
Shuffle test and the Preference-based Matched Estimation (PME) test to
distinguish the effects of influence and homophily. The results indicate that
the visual content of each user's photographs are temporally, although not
necessarily causally, correlated with the photographs of their friends, which
may suggest the effect of influence. Our paper demonstrates that Facebook
photographs exhibit diverse cultural lifestyles and preferences and that the
social interaction mediated through the visual channel in social media can be
an effective mechanism for cultural diffusion.Comment: 10 pages, To appear in ICWSM 2017 (Full Paper
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