The success of media sharing and social networks has led to the availability of extremely large quantities of images that are tagged by users. The need of methods to manage efficiently and effectively the combination of media and metadata poses significant challenges. In particular, automatic image annotation of social images has become an important research topic for the multimedia community.
Detected tags in an image using Nearest-Neighbor Methods for Tag Refinement
We propose and thoroughly evaluate the use of nearest-neighbor methods for tag refinement. We performed extensive and rigorous evaluation using two standard large-scale datasets to show that the performance of these methods is comparable with that of more complex and computationally intensive approaches. Differently from these latter approaches, nearest-neighbor methods can be applied to ‘web-scale’ data.
Here we make available the code and the metadata for NUS-WIDE-240K.
Nuswide-240K dataset metadata (JSON format, about 25MB). A subset of 238,251 images from NUS-WIDE-270K that we retrieved from Flickr with users data. Note that NUS is now releasing the full image set subject to an agreement and disclaimer form.
If you use this data, please cite the paper as follows:
author = "Uricchio, Tiberio and Ballan, Lamberto and Bertini,
Marco and Del Bimbo, Alberto",
title = "An evaluation of nearest-neighbor methods for tag refinement",
booktitle = "Proc. of IEEE International Conference on Multimedia \& Expo (ICME)",
month = "jul",
year = "2013",
address = "San Jose, CA, USA",
url = "http://www.micc.unifi.it/publications/2013/UBBD13"
The IM3I project addresses the needs of a new generation of media and communication industry that has to confront itself not only with changing technologies, but also with the radical change in media consumption behaviour. IM3I will enable new ways of accessing and presenting media content to users, and new ways for users to interact with services, offering a natural and transparent way to deal with the complexities of interaction, while hiding them from the user.
Daphnis: IM3I multimedia content based retrieval interface
With the explosion in the volume of digital content being generated, there is a pressing need for highly customisable interfaces tailored according to both user profiles and specific types of search. IM3I aims to provide the creative media sector with new ways of searching, summarising and visualising large multimedia archives. IM3I will provide a service-oriented architecture that allow multiple viewpoints upon multimedia data that are available in a repository, and provide better ways to interact and share rich media. This paves the way for a multimedia information management platform which is more flexible, adaptable and customisable than current repository software. This in turn enables new opportunities for content owners to exploit their digital assets.
The IM3I project addresses the needs of a new generation of media and communication industry that has to confront itself not only with changing technologies, but also with the radical change in media consumption behaviour.
IM3I will enable new ways of accessing and presenting media content to users, and new ways for users to interact with services, offering a natural and transparent way to deal with the complexities of interaction, while hiding them from the user.
But most of all, designed according to a SOA paradigm, IM3I will also define an enabling technology capable of integrating into existing networks, which will support organisations and users in developing their content related services.
The VidiVideo project takes on the challenge of creating a substantially enhanced semantic access to video, implemented in a search engine. The outcome of the project is an audio-visual search engine, composed of two parts: an automatic annotation part, that runs off-line, where detectors for more than 1000 semantic concepts are collected in a thesaurus to process and automatically annotate the video and an interactive part that provides a video search engine for both technical and non-technical users.
Andromeda - Vidivideo graph based video browsing
Video plays a key role in the news, cultural heritage documentaries and surveillance, and it is a natural form of communication for the Internet and mobile devices. The massive increase in digital audio-visual information poses high demands on advanced storage and search engines for consumers and professional archives.
Video search engines are the product of progress in many technologies: visual and audio analysis, machine learning techniques, as well as visualization and interaction. At present the state-of-the-art systems are able to annotate automatically only a limited set of semantic concepts, and the retrieval is allowed using only a keyword-based approach based on a lexicon.
The VidiVideo project takes on the challenge of creating a substantially enhanced semantic access to video, implemented in a search engine.
The outcome of the project is an audio-visual search engine, composed of two parts: a automatic annotation part, that runs off-line, where detectors for more than 1000 semantic concepts are collected in a thesaurus to process and automatically annotate the video and an interactive part that provides a video search engine for both technical and non-technical users.
The automatic annotation part of the system performs audio and video segmentation, speech recognition, speaker clustering and semantic concept detection.
The VidiVideo system has achieved the highest performance in the most important object and concept recognition international contests (PASCAL VOC and TRECVID).
The interactive part provides two applications: a desktop-based and a web-based search engines. The system permits different query modalities (free text, natural language, graphical composition of concepts using boolean and temporal relations and query by visual example) and visualizations, resulting in an advanced tool for retrieval and exploration of video archives for both technical and non-technical users in different application fields. In addition the use of ontologies (instead of simple keywords) permits to exploit semantic relations between concepts through reasoning, extending the user queries.
The off-line annotation part has been implemented in C++ on the Linux platform, and takes advantage of the low-cost processing power provided by GPUs on consumer graphics cards.
The web-based system is based on the Rich Internet Application paradigm, using a client side Flash virtual machine. RIAs can avoid the usual slow and synchronous loop for user interactions. This allows to implement a visual querying mechanism that exhibits a look and feel approaching that of a desktop environment, with the fast response that is expected by users. The search results are in RSS 2.0 XML format, while videos are streamed using the RTMP protocol.
The availability of measures of appearance of trademarks and logos in a video is important in fields of marketing and sponsoring. These statistics can, in fact, be used by the sponsors to estimate the number TV viewers that noticed them and then evaluate the effects of the sponsorship. The goal of this project is to create a semi-automatic system for detection, tracking and recognition of pre-defined brands and trademarks in broadcast television. The number of appearances of a logo, its position, size and duration will be recorded to derive indexes and statistics that can be used for marketing analysis.
Automatic trademark detection and recognition in sports videos
To obtain a technique that is sufficiently robust to partial occlusions and deformations, we use local neighborhood descriptors of salient points (SIFT features) as a compact representation of the important aspects and local texture in trademarks. By combining the results of local point-based matching we are able to detect and recognize entire trademarks. The determination of whether a video frame contains a reference trademark is made by thresholding the normalized-match score (the ratio of SIFT points of the trademark that have been matched to the frame). Finally, we compute a robust estimate of the point cloud in order to localize the trademark and to approximate its area.
The recognition of events in videos is a relevant and challenging task of automatic semantic video analysis. At present one of the most successful frameworks, used for object recognition tasks, is the bag-of-words (BoW) approach. However it does not model the temporal information of the video stream. We are working at a novel method to introduce temporal information within the BoW approach by modeling a video clip as a sequence of histograms of visual features, computed from each frame using the traditional BoW model.
Video event classification using bag-of-words and string kernels
The sequences are treated as strings where each histogram is considered as a character. Event classification of these sequences of variable size, depending on the length of the video clip, are performed using SVM classifiers with a string kernel (e.g using the Needlemann-Wunsch edit distance). Experimental results, performed on two domains, soccer video and TRECVID 2005, demonstrate the validity of the proposed approach.
Building a general human activity recognition and classification system is a challenging problem, because of the variations in environment, people and actions. In fact environment variation can be caused by cluttered or moving background, camera motion, illumination changes. People may have different size, shape and posture appearance. Recently, interest-points based models have been successfully applied to the human action classification problem, because they overcome some limitations of holistic models such as the necessity of performing background subtraction and tracking. We are working at a novel method based on the visual bag-of-words model and on a new spatio-temporal descriptor.
Human action categorization in unconstrained videos
First, we define a new 3D gradient descriptor that combined with optic flow outperforms the state-of-the-art, without requiring fine parameter tuning. Second, we show that for spatio-temporal features the popular k-means algorithm is insufficient because cluster centers are attracted by the denser regions of the sample distribution, providing a non-uniform description of the feature space and thus failing to code other informative regions. Therefore, we apply a radius-based clustering method and a soft assignment that considers the information of two or more relevant candidates. This approach generates a more effective codebook resulting in a further improvement of classification performances. We extensively test our approach on standard KTH and Weizmann action datasets showing its validity and outperforming other recent approaches.