Paolo Bolettieri

Detection of images with adult content for parental control on mobile devices

In this paper we present a prototype for parental control that detects images with adult content received on a mobile device. More specifically, the application that we developed is able to intercept images received through various communication channels (bluetooth, MMS) on mobile devices based on the Symbian#8482; operating systems. Once intercepted, the images are analysed by the component of the system that automatically classify images with explicit sexual content. At the current stage the application that intercept images runs on the mobile device, the classifier runs on a remote server.

Automatic metadata extraction and indexing for reusing e-learning multimedia objects

In this paper we present the architecture of a Digital Library for enabling the reusing of audiovisual documents in an e-Learning context. The reuse of Learning Objects is based on automatically extracted descriptors carrying a semantic meaning for the professional that uses these Learning Objects to prepare new interactive multimedia lectures. The presented system is based on MILOS, a general purpose Multimedia Content Management System created to support design and effective implementation of digital library applications. MILOS supports the storage andcontent based retrieval of any multimedia documents whose descriptions are provided by using arbitrary metadata models represented in XML. The objective is to demonstrate the reuse of digital content, as video documents or Power Point presentations, by exploiting existing technologies for automatic extraction of metadata (OCR, speech recognition, cut detection, MPEG-7 visual descriptors, etc.). The search interface assists the user of the system in the retrieval the multimedia objects in the collection, by combining full-text retrieval on text extracted and metadata, and similarity search on the MPEG-7 visual descriptors.

Combining local and global visual feature similarity using a text search engine

In this paper we propose a novel approach that allows processing image content based queries expressed as arbitrary combinations of local and global visual features, by using a single index realized as an inverted file. The index was implemented on top of the Lucene retrieval engine. This is particularly useful to allow people to efficiently and interactively check the quality of the retrieval result by exploiting combinations of various features when using content based retrieval systems.

Recognizing Landmarks Using Automated Classification Techniques: Evaluation of Various Visual Features

In this paper, the performance of several visual features is evaluated in automatically recognizing landmarks (monuments, statues, buildings, etc.) in pictures. A number of landmarks were selected for the test. Pictures taken from a test set were classified automatically trying to guess which landmark they contained. We evaluated both global and local features. As expected, local features performed better given their capability of being less affected to visual variations and given that landmarks are mainly static objects that generally also maintain static local features. Between the local features, SIFT outperformed SURF and ColorSIFT.

Using MILOS to build a multimedia digital library application: the photobook experience

The digital library field is recently broadening its scope of applicability and it is also continuously adapting to the frequent changes occurring in the internet society. Accordingly, digital libraries are slightly moving from a controlled environment accessible only to professionals and domain-experts, to environments accessible to casual users that want to exploit the potentialities offered by the digital library technology. These new trends require, for instance, new search paradigms to be offered, new media content to be managed, and new description extraction techniques to be used. Building digital library applications, and effectively adapting them to new emerging trends, requires to develop a platform that offers standard and powerful building blocks to support application developers. In this paper we discuss our experience of using MILOS, a multimedia content management system oriented to the construction of digital libraries, to build a demanding application dedicated to non-professional users. Specifically, we discuss the design and implementation of an on-line photo album (PhotoBook), which is a digital library application that allows people to manage their own photos, to share them with friends, and to make them publicly available and searchable. PhotoBook, uses a complex internal metadata schema (MPEG-7) and allows users to simply express complex queries (combining similarity search and fielded search), enabling them to retrieve material of interest even if metadata are imprecise or missing.

Large Scale Image Retrieval Using Vector of Locally Aggregated Descriptors

Vector of locally aggregated descriptors VLAD is a promising approach for addressing the problem of image search on a very large scale. This representation is proposed to overcome the quantization error problem faced in Bag-of-Words BoW representation. However, text search engines have not be used yet for indexing VLAD given that it is not a sparse vector of occurrence counts. For this reason BoW approach is still the most widely adopted method for finding images that represent the same object or location given an image as a query and a large set of images as dataset. In this paper, we propose to enable inverted files of standard text search engines to exploit VLAD representation to deal with large-scale image search scenarios. We show that the use of inverted files with VLAD significantly outperforms BoW in terms of efficiency and effectiveness on the same hardware and software infrastructure.

Landmark recognition in VISITO: VIsual Support to Interactive TOurism in Tuscany

We present the VIsual Support to Interactive TOurism in Tuscany (VISITO Tuscany) project which offers an interactive guide for tourists visiting cities of art accessible via smartphones. The peculiarity of the system is that user interaction is mainly obtained by the use of images -- In order to receive information on a particular monument users just have to take a picture of it. VISITO Tuscany, using techniques of image analysis and content recognition, automatically recognize the photographed monuments and pertinent information is displayed to the user. In this paper we illustrate how the use of landmarks recognition from mobile devices can provide the tourist with relevant and customized information about various type of objects in cities of art.

A digital library framework for reusing e-learning video documents

The objective of this paper is to demonstrate the reuse of digital content, as video documents or PowerPoint presentations, by exploiting existing technologies for automatic extraction of metadata (OCR, speech recognition, cut detection, MPEG-7 visual descriptors, etc.). The multimedia documents and the extracted metadata are then indexed and managed by the Multimedia Content Management System (MCMS) MILOS, specifically developed to support design and effective implementation of digital library applications. As a result, the indexed digital material can be retrieved by means of content based retrieval on the text extracted and on the MPEG-7 visual descriptors (via similarity search), assisting the user of the e-Learning Library (student or teacher) to retrieve the items not only on the basic bibliographic metadata (title, author, etc.).

Social Media Image Recognition for Food Trend Analysis

An increasing number of people share their thoughts and the images of their lives on social media platforms. People are exposed to food in their everyday lives and share on-line what they are eating by means of photos taken to their dishes. The hashtag #foodporn is constantly among the popular hashtags in Twitter and food photos are the second most popular subject in Instagram after selfies. The system that we propose, WorldFoodMap, captures the stream of food photos from social media and, thanks to a CNN food image classifier, identifies the categories of food that people are sharing. By collecting food images from the Twitter stream and associating food category and location to them, WorldFoodMap permits to investigate and interactively visualize the popularity and trends of the shared food all over the world.

Quick and Easy Implementation of Approximate Similarity Search with Lucene

Similarity search technique has been proved to be an effective way for retrieving multimedia content. However, as the amount of available multimedia data increases, the cost of developing from scratch a robust and scalable system with content-based image retrieval facilities is quite prohibitive.