Giuseppe Serra

We release the MATLAB implementation of the copy-move detection approach presented in Amerini et al., TIFS 2011. We provide some scripts to replicate the detection experiments reported in our paper, and also some functions for copy-move detection in a single image. Please note that our code use several public functions and libraries developed by other authors; regarding these files, for any problem or license information, please refer to the respective authors.

MICC-CMFD-1.0.zip – released May 8, 2012   (tested on Linux Ubuntu 10.04)

If you use our software or these datasets, please cite the paper: I. Amerini, L. Ballan, R. Caldelli, A. Del Bimbo, G. Serra. “A SIFT-based forensic method for copy-move attack detection and transformation recovery”, IEEE Transactions on Information Forensics and Security, vol. 6, iss. 3, pp. 1099-1110, 2011.

“Effective Codebooks for Human Action Representation and Classification in Unconstrained Videos” by L. Ballan, M. Bertini, A. Del Bimbo, L. Seidenari and G. Serra has been accepted for publication in the IEEE Transactions on Multimedia.

Recognition and classification of human actions for annotation of unconstrained video sequences has proven to be challenging because of the variations in the environment, appearance of actors, modalities in which the same action is performed by different persons, speed and duration and points of view from which the event is observed. This variability reflects in the difficulty of defining effective descriptors and deriving appropriate and effective codebooks for action categorization. In
this paper we propose a novel and effective solution to classify human actions in unconstrained videos. It improves on previous contributions through the definition of a novel local descriptor
that uses image gradient and optic flow to respectively model the appearance and motion of human actions at interest point regions. In the formation of the codebook we employ radiusbased clustering with soft assignment in order to create a rich vocabulary that may account for the high variability of human actions. We show that our solution scores very good performance with no need of parameter tuning. We also show that a strong reduction of computation time can be obtained by applying codebook size reduction with Deep Belief Networks with little loss of accuracy.

IRCDL is a yearly appointment for Italian researchers on Digital Libraries and related topics.
This year the focus of IRCDL is on legacy and cultural heritage material.
Indeed, Digital Library Systems are getting more and more mature and largely deployed. Not only they have to ensure users effective and personalized access to information but it is also now time to face the need for smoothly processing and including in the DL repositories the available legacy and cultural heritage documents, in addition to born-digital ones.
This calls for the ability to deal with compound objects in different media, to provide uniform solutions and methodologies across different cultural heritage institutions, and to take into account preservation, restoration, and curation.
The IRCDL conferences have been launched and initially sponsored by DELOS, an EU FP6 Network of Excellence on digital libraries (http://www.delos.info/) together with the Department of Information Engineering of the University of Padua.
Over the years IRCDL has become a self-sustainable event sponsored and supported by the Italian Digital Library Research Community.

- Submission Deadline: December 18, 2011

The paper  ”Enriching and Localizing Semantic Tags in Internet Videos“ has been accepted by ACM Multimedia 2011.

Tagging of multimedia content is becoming more and more widespread as web 2.0 sites, like Flickr and Facebook for images, YouTube and Vimeo for videos, have popularized tagging functionalities among their users. These user-generated tags are used to retrieve multimedia content, and to ease browsing and exploration of media collections, e.g. using tag clouds. However, not all media are equally tagged by users: using the current browsers is easy to tag a single photo, and even tagging a part of a photo, like a face, has become common in sites like Flickr and Facebook; on the other hand tagging a video sequence is more complicated and time consuming, so that users just tend to tag the overall content of a video. In this paper we present a system for automatic video annotation that increases the number of tags originally provided by users, and localizes them temporally, associating tags to shots. This approach exploits collective knowledge embedded in tags and Wikipedia, and visual similarity of keyframes and images uploaded to social sites like YouTube and Flickr.

Our ICIAP paper “Space-time Zernike Moments and Pyramid Kernel Descriptors for Action Classification” is available online.

Action recognition in videos is a relevant and challengingtask of automatic semantic video analysis. Most successful approachesexploit local space-time descriptors. These descriptors are usually care-fully engineered in order to obtain feature invariance to photometric andgeometric variations. The main drawback of space-time descriptors ishigh dimensionality and eciency. In this paper we propose a novel de-scriptor based on 3D Zernike moments computed for space-time patches.Moments are by construction not redundant and therefore optimal forcompactness. Given the hierarchical structure of our descriptor we pro-pose a novel similarity procedure that exploits this structure comparingfeatures as pyramids. The approach is tested on a public dataset andcompared with state-of-the art descriptors.