24,036 research outputs found
Ranking News-Quality Multimedia
News editors need to find the photos that best illustrate a news piece and
fulfill news-media quality standards, while being pressed to also find the most
recent photos of live events. Recently, it became common to use social-media
content in the context of news media for its unique value in terms of immediacy
and quality. Consequently, the amount of images to be considered and filtered
through is now too much to be handled by a person. To aid the news editor in
this process, we propose a framework designed to deliver high-quality,
news-press type photos to the user. The framework, composed of two parts, is
based on a ranking algorithm tuned to rank professional media highly and a
visual SPAM detection module designed to filter-out low-quality media. The core
ranking algorithm is leveraged by aesthetic, social and deep-learning semantic
features. Evaluation showed that the proposed framework is effective at finding
high-quality photos (true-positive rate) achieving a retrieval MAP of 64.5% and
a classification precision of 70%.Comment: To appear in ICMR'1
Outlier Mining Methods Based on Graph Structure Analysis
Outlier detection in high-dimensional datasets is a fundamental and challenging problem across disciplines that has also practical implications, as removing outliers from the training set improves the performance of machine learning algorithms. While many outlier mining algorithms have been proposed in the literature, they tend to be valid or efficient for specific types of datasets (time series, images, videos, etc.). Here we propose two methods that can be applied to generic datasets, as long as there is a meaningful measure of distance between pairs of elements of the dataset. Both methods start by defining a graph, where the nodes are the elements of the dataset, and the links have associated weights that are the distances between the nodes. Then, the first method assigns an outlier score based on the percolation (i.e., the fragmentation) of the graph. The second method uses the popular IsoMap non-linear dimensionality reduction algorithm, and assigns an outlier score by comparing the geodesic distances with the distances in the reduced space. We test these algorithms on real and synthetic datasets and show that they either outperform, or perform on par with other popular outlier detection methods. A main advantage of the percolation method is that is parameter free and therefore, it does not require any training; on the other hand, the IsoMap method has two integer number parameters, and when they are appropriately selected, the method performs similar to or better than all the other methods tested.Peer ReviewedPostprint (published version
Interactively Test Driving an Object Detector: Estimating Performance on Unlabeled Data
In this paper, we study the problem of `test-driving' a detector, i.e.
allowing a human user to get a quick sense of how well the detector generalizes
to their specific requirement. To this end, we present the first system that
estimates detector performance interactively without extensive ground truthing
using a human in the loop. We approach this as a problem of estimating
proportions and show that it is possible to make accurate inferences on the
proportion of classes or groups within a large data collection by observing
only of samples from the data. In estimating the false detections (for
precision), the samples are chosen carefully such that the overall
characteristics of the data collection are preserved. Next, inspired by its use
in estimating disease propagation we apply pooled testing approaches to
estimate missed detections (for recall) from the dataset. The estimates thus
obtained are close to the ones obtained using ground truth, thus reducing the
need for extensive labeling which is expensive and time consuming.Comment: Published at Winter Conference on Applications of Computer Vision,
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