2016, Articolo in rivista, ENG
Amato G., Bolettieri P., Falchi F., Vadicamo L.
In this paper, we present a system for visually retrieving ancient inscriptions, developed in the context of the ongoing Europeana network of Ancient Greek and Latin Epigraphy (EAGLE) EU Project. The system allows the user in front of an inscription (e.g, in a museum, street, archaeological site) or watching a reproduction (e.g., in a book, from a monitor), to automatically recognize the inscription and obtain information about it just using a smart-phone or a tablet. The experimental results show that the Vector of Locally Aggregated Descriptors is a promising encoding strategy for performing visual recognition in this specific context.
2015, Rapporto tecnico, ENG
Bolettieri P.
This report describes the final implementation of the Image Retrieval System infrastructure developed for the EAGLE (Europeana network of Ancient Greek and Latin Epigraphy) project. The EAGLE project is gathering a comprehensive collection of inscriptions (about 80 % of the surviving material) and making it accessible through a user-friendly portal, which supports searching and browsing of the epigraphic material. In this document we will describe the Image Retrieval System and its API.
2015, Contributo in atti di convegno, ENG
Amato G.; Bolettieri P.; Falchi F.; Rabitti F.; Vadicamo L.
In this paper, we present a system for visually retrieving an- cient inscriptions, developed in the context of the ongoing Europeana network of Ancient Greek and Latin Epigraphy (EAGLE) EU Project. The system allows the user in front of an inscription (e.g, in a museum, street, archaeological site) or watching a reproduction (e.g., in a book, from a monitor), to automatically recognize the inscription and obtain information about it just using a smart-phone or a tablet. The experi- mental results show that the Vector of Locally Aggregated Descriptors is a promising encoding strategy for performing visual recognition in this specific context.
2015, Articolo in rivista, ENG
Amato G.; Falchi F.; Gennaro C.
Content-based image classification is a wide research field that addresses the landmark recognition problem. Among the many classification techniques proposed, the k-nearest neighbor (kNN) is one of the most simple and widely used methods. In this article, we use kNN classification and landmark recognition techniques to address the problem of monument recognition in images. We propose two novel approaches that exploit kNN classification technique in conjunction with local visual descriptors. The first approach is based on a relaxed definition of the local feature based image to image similarity and allows standard kNN classification to be efficiently executed with the support of access methods for similarity search. The second approach uses kNN classification to classify local features rather than images. An image is classified evaluating the consensus among the classification of its local features. In this case, access methods for similarity search can be used to make the classification approach efficient. The proposed strategies were extensively tested and compared against other state-of-the-art alternatives in a monument and cultural heritage landmark recognition setting. The results proved the superiority of our approaches. An additional relevant contribution of this work is the exhaustive comparison of various types of local features and image matching solutions for recognition of monuments and cultural heritage related landmarks.
2015, Rapporto tecnico, ENG
Carrara F.; Amato G.; Falchi F.; Gennaro C.
In this work, a local feature based background modelling for background-foreground feature segmentation is presented. In local feature based computer vision applications, a local feature based model presents advantages with respect to classical pixel-based ones in terms of informativeness, robustness and segmentation performances. The method discussed in this paper is a block-wise background modelling where we propose to store the positions of only most frequent local feature configurations for each block. Incoming local features are classified as background or foreground depending on their position with respect to stored configurations. The resulting classification is refined applying a block-level analysis. Experiments on public dataset were conducted to compare the presented method to classical pixel-based background modelling
2013, Contributo in atti di convegno, ENG
Giuseppe A.; Falchi F.; Gennaro C.
The state-of-the-art algorithms for large visual content recognition and content based similarity search today use the Bag of Features" (BoF) or Bag of Words (BoW) approach. The idea, borrowed from text retrieval, enables the use of inverted files. A very well known issue with the BoF approach is that the query images, as well as the stored data, are described with thousands of words. This poses obvious efficiency problems when using inverted files to perform efficient image matching. In this paper, we propose and compare various techniques to reduce the number of words describing an image to improve efficiency.
2011, Rapporto di progetto (Project report), ENG
Amato G., Bolettieri P., Falchi F., Gennaro C.
In this document, the developing of the software component for approximate image matching is reported. This document is an output of the Activity A4.3 "Indici efficienti per il matching approssimato e la classificazione delle immagini" of the VISITO Tuscany project. The output of Activity 4 .3 is this document and the software component made available inside on the VISITO Tuscany project wiki. The software component is optimized for working on images related to cultural heritage objects.
2011, Rapporto di progetto (Project report), ENG
Amato G., Falchi F., Bolettieri P.
In this document, the developing of the software component for approximate image matching is reported. This document is an output of the Activity A4.1 "Strumenti per il matching approssimato delle immagini" of the VISITO Tuscany project. Planned from month 4 to month 12, the activity started earlier on month 3. The output of Activity 4.1 is this document and the software component made available inside on the VISITO Tuscany project wiki. The software component is optimized for working on images related to cultural heritage objects.
2011, Contributo in atti di convegno, ENG
Amato G.; Falchi F.
Applications of image content recognition, as for instance landmark recognition, can be obtained by using techniques of kN N classifications based on the use of local image features, such as SIFT or SURF. Quality of image classification can be improved by defining geometric consistency check rules based on space transformations of the scene depicted in images. However, this prevents the use of state of the art access methods for similarity searching and sequential scan of the images in the training sets has to be executed in order to perform classification. In this paper we propose a technique that allows one to use access methods for similarity searching, such as those exploiting metric space properties, in order to perform kN N classification with geometric consistency checks. We will see that the proposed approach, in addition to offer an obvious efficiency improvement, surprisingly offers also an improvement of the effectiveness of the classification.
2010, Rapporto di progetto (Project report), ENG
Amato G.; Falchi F.; Bolettieri P.
In this document, the developing of the software component for approximate image matching is reported. This document is an output of the Activity A4.1 "Strumenti per il matching approssimato delle immagini" of the VISITO Tuscany project. Planned from month 4 to month 12, the activity started earlier on month 3. The output of Activity 4.1 is this document and the software component made available inside on the VISITO Tuscany project wiki. The software component is optimized for working on images related to cultural heritage objects.