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

The ALHAMBRA Survey: Bayesian Photometric Redshifts with 23 bands for 3 squared degrees

The ALHAMBRA (Advance Large Homogeneous Area Medium Band Redshift Astronomical) survey has observed 8 different regions of the sky, including sections of the COSMOS, DEEP2, ELAIS, GOODS-N, SDSS and Groth fields using a new photometric system with 20 contiguous ~ $300\AA$ filters covering the optical range, combining them with deep $JHKs$ imaging. The observations, carried out with the Calar Alto 3.5m telescope using the wide field (0.25 sq. deg FOV) optical camera LAICA and the NIR instrument Omega-2000, correspond to ~700hrs on-target science images. The photometric system was designed to maximize the effective depth of the survey in terms of accurate spectral-type and photo-zs estimation along with the capability of identification of relatively faint emission lines. Here we present multicolor photometry and photo-zs for ~438k galaxies, detected in synthetic F814W images, complete down to I~24.5 AB, taking into account realistic noise estimates, and correcting by PSF and aperture effects with the ColorPro software. The photometric ZP have been calibrated using stellar transformation equations and refined internally, using a new technique based on the highly robust photometric redshifts measured for emission line galaxies. We calculate photometric redshifts with the BPZ2 code, which includes new empirically calibrated templates and priors. Our photo-zs have a precision of $dz/(1+z_s)=1$ for I<22.5 and 1.4% for 22.5<I<24.5. Precisions of less than 0.5% are reached for the brighter spectroscopic sample, showing the potential of medium-band photometric surveys. The global $P(z)$ shows a mean redshift =0.56 for I=0.86 for I<24.5 AB. The data presented here covers an effective area of 2.79 sq. deg, split into 14 strips of 58.5'x15.5' and represents ~32 hrs of on-target.Comment: The catalog data and a full resolution version of this paper is available at https://cloud.iaa.csic.es/alhambra

A PatchMatch-based Dense-field Algorithm for Video Copy-Move Detection and Localization

We propose a new algorithm for the reliable detection and localization of video copy-move forgeries. Discovering well crafted video copy-moves may be very difficult, especially when some uniform background is copied to occlude foreground objects. To reliably detect both additive and occlusive copy-moves we use a dense-field approach, with invariant features that guarantee robustness to several post-processing operations. To limit complexity, a suitable video-oriented version of PatchMatch is used, with a multiresolution search strategy, and a focus on volumes of interest. Performance assessment relies on a new dataset, designed ad hoc, with realistic copy-moves and a wide variety of challenging situations. Experimental results show the proposed method to detect and localize video copy-moves with good accuracy even in adverse conditions

An Evaluation of Popular Copy-Move Forgery Detection Approaches

A copy-move forgery is created by copying and pasting content within the same image, and potentially post-processing it. In recent years, the detection of copy-move forgeries has become one of the most actively researched topics in blind image forensics. A considerable number of different algorithms have been proposed focusing on different types of postprocessed copies. In this paper, we aim to answer which copy-move forgery detection algorithms and processing steps (e.g., matching, filtering, outlier detection, affine transformation estimation) perform best in various postprocessing scenarios. The focus of our analysis is to evaluate the performance of previously proposed feature sets. We achieve this by casting existing algorithms in a common pipeline. In this paper, we examined the 15 most prominent feature sets. We analyzed the detection performance on a per-image basis and on a per-pixel basis. We created a challenging real-world copy-move dataset, and a software framework for systematic image manipulation. Experiments show, that the keypoint-based features SIFT and SURF, as well as the block-based DCT, DWT, KPCA, PCA and Zernike features perform very well. These feature sets exhibit the best robustness against various noise sources and downsampling, while reliably identifying the copied regions.Comment: Main paper: 14 pages, supplemental material: 12 pages, main paper appeared in IEEE Transaction on Information Forensics and Securit

Two-View Matching with View Synthesis Revisited

Wide-baseline matching focussing on problems with extreme viewpoint change is considered. We introduce the use of view synthesis with affine-covariant detectors to solve such problems and show that matching with the Hessian-Affine or MSER detectors outperforms the state-of-the-art ASIFT. To minimise the loss of speed caused by view synthesis, we propose the Matching On Demand with view Synthesis algorithm (MODS) that uses progressively more synthesized images and more (time-consuming) detectors until reliable estimation of geometry is possible. We show experimentally that the MODS algorithm solves problems beyond the state-of-the-art and yet is comparable in speed to standard wide-baseline matchers on simpler problems. Minor contributions include an improved method for tentative correspondence selection, applicable both with and without view synthesis and a view synthesis setup greatly improving MSER robustness to blur and scale change that increase its running time by 10% only.Comment: 25 pages, 14 figure

Anatomy-specific classification of medical images using deep convolutional nets

Automated classification of human anatomy is an important prerequisite for many computer-aided diagnosis systems. The spatial complexity and variability of anatomy throughout the human body makes classification difficult. "Deep learning" methods such as convolutional networks (ConvNets) outperform other state-of-the-art methods in image classification tasks. In this work, we present a method for organ- or body-part-specific anatomical classification of medical images acquired using computed tomography (CT) with ConvNets. We train a ConvNet, using 4,298 separate axial 2D key-images to learn 5 anatomical classes. Key-images were mined from a hospital PACS archive, using a set of 1,675 patients. We show that a data augmentation approach can help to enrich the data set and improve classification performance. Using ConvNets and data augmentation, we achieve anatomy-specific classification error of 5.9 % and area-under-the-curve (AUC) values of an average of 0.998 in testing. We demonstrate that deep learning can be used to train very reliable and accurate classifiers that could initialize further computer-aided diagnosis.Comment: Presented at: 2015 IEEE International Symposium on Biomedical Imaging, April 16-19, 2015, New York Marriott at Brooklyn Bridge, NY, US