322,870 research outputs found
Ancient Coin Classification Using Graph Transduction Games
No full textRecognizing the type of an ancient coin requires theoretical expertise and years of experience in the field of numismatics. Our goal in this work is automatizing this time-consuming and demanding task by a visual classification frame-work. Specifically, we propose to model ancient coin image classification using Graph Transduction Games (GTG). GTG casts the classification problem as a non-cooperative game where the players (the coin images) decide their strategies (class labels) according to the choices made by the others, which results with a global consensus at the final labeling. Experiments are conducted on the only publicly available dataset which is composed of 180 images of 60 types of Roman coins. We demonstrate that our approach outperforms the literature work on the same dataset with the classification accuracy of 73.6% and 87.3% when there are one and two images per class in the training set, respectively
Two sides of the same coin: Improved ancient coin classification using Graph Transduction Games
No full textIn this work we tackle the problem of automatic recognition of ancient coin types using a semisupervised learning method, namely Graph Transduction Games. Such problem is complex, mainly due to the low inter-class and large intra-class variations and the task becomes even more complex due to lack of labeled large datasets from certain ancient ages. In this paper we propose a new dataset which is chiefly the extension of a previous one both in terms of quantity and diversity. Moreover, we propose a game-theoretic model that exploits both sides of a coin to achieve higher classification accuracy. We experimentally demonstrate that proposed approach brings performance improvement in this complex task even when few number of labelled images are available
Gli inumati di Ballabio. Prime osservazioni antropologiche
No full textViene presentata l'analisi antropologica dei resti scheletrici umani provenienti da una necropoli dell'età del Bronzo Antico rinvenuta nel 2004 a Ballabio (Como)
Semantic Motif Segmentation of Archaeological Fresco Fragments
No full textArchaeological fragment processing is crucial to support the analysis of pictorial contents of broken artifacts. In this paper, we focus on the unexplored task of semantic segmentation of fresco fragments. This task enables the extraction of semantic information from a fragment, facilitating subsequent tasks like fragment classification or reassembly. We introduce a semantic segmentation dataset of fresco fragments acquired at the Pompeii Archeological Site, accompanied by baseline models. Additionally, we introduce a supplementary task of fragment cleaning, providing a dataset with the detection of manual annotations of archaeological marks that require restoration before further analysis. Our experiments, using standard metrics and state-of-the-art baselines, demonstrate that semantic segmentation of fresco fragments is feasible, paving the way toward more complex activities that require a semantic understanding of fragmented artifacts
Semantic Motif Segmentation of Archaeological Fresco Fragments
No full textArchaeological fragment processing is crucial to support the analysis of pictorial contents of broken artifacts. In this paper, we focus on the unexplored task of semantic segmentation of fresco fragments. This task enables the extraction of semantic information from a fragment, facilitating subsequent tasks like fragment classification or reassembly. We introduce a semantic segmentation dataset of fresco fragments acquired at the Pompeii Archeological Site, accompanied by baseline models. Additionally, we introduce a supplementary task of fragment cleaning, providing a dataset with the detection of manual annotations of archaeological marks that require restoration before further analysis. Our experiments, using standard metrics and state-of-the-art baselines, demonstrate that semantic segmentation of fresco fragments is feasible, paving the way toward more complex activities that require a semantic understanding of fragmented artifacts
Reassembling Broken Objects Using Breaking Curves
No full textReassembling 3D broken objects is a challenging task. A robust solution that generalizes well must deal with diverse patterns associated with different types of broken objects. We propose a method that tackles the pairwise assembly of 3D point clouds, that is agnostic on the type of object, and that relies solely on their geometrical information, without any prior information on the shape of the reconstructed object. The method receives two point clouds as input and segments them into regions using detected closed boundary contours, known as breaking curves. Possible alignment combinations of the regions of each broken object are evaluated and the best one is selected as the final alignment. Experiments were carried out both on available 3D scanned objects and on a recent benchmark for synthetic broken objects. Results show that our solution performs well in reassembling different kinds of broken objects. The code is available at https://github.com/RePAIRProject/AAFR
Biclustering with Dominant Sets
No full textBiclustering can be defined as the simultaneous clustering of rows and columns in a data matrix and it has been recently applied to many scientific scenarios such as bioinformatics, text analysis and computer vision to name a few. In this paper we propose a novel biclustering approach, that is based on the concept of dominant-set clustering and extends such algorithm to the biclustering problem. In more detail, we propose a novel encoding of the biclustering problem as a graph so to use the dominant set concept to analyse rows and columns simultaneously. Moreover, we extend the Dominant Set Biclustering approach to facilitate the insertion of prior knowledge that may be available on the domain. We evaluated the proposed approach on a synthetic benchmark and on two computer vision tasks: multiple structure recovery and region-based correspondence. The empirical evaluation shows that the method achieves promising results that are comparable to the state-of-the-art and that outperforms competitors in various cases.Biclustering can be defined as the simultaneous clustering of rows and columns in a data matrix and it has been recently applied to many scientific scenarios such as bioinformatics, text analysis and computer vision to name a few. In this paper we propose a novel biclustering approach, that is based on the concept of dominant-set clustering and extends such algorithm to the biclustering problem. In more detail, we propose a novel encoding of the biclustering problem as a graph so to use the dominant set concept to analyse rows and columns simultaneously. Moreover, we extend the Dominant Set Biclustering approach to facilitate the insertion of prior knowledge that may be available on the domain. We evaluated the proposed approach on a synthetic benchmark and on two computer vision tasks: multiple structure recovery and region-based correspondence. The empirical evaluation shows that the method achieves promising results that are comparable to the state-of-the-art and that outperforms competitors in various cases. (C) 2020 Elsevier Ltd. All rights reserved
Transductive Visual Verb Sense Disambiguation
No full textVerb Sense Disambiguation is a well-known task in NLP, the aim is to find the correct sense of a verb in a sentence. Recently, this problem has been extended in a multimodal scenario, by exploiting both textual and visual features of ambiguous verbs leading to a new problem, the Visual Verb Sense Disambiguation (VVSD). Here, the sense of a verb is assigned considering the content of an image paired with it rather than a sentence in which the verb appears. Annotating a dataset for this task is more complex than textual disambiguation, because assigning the correct sense to a pair of requires both non-trivial linguistic and visual skills. In this work, differently from the literature, the VVSD task will be performed in a transductive semi-supervised learning (SSL) setting, in which only a small amount of labeled information is required, reducing tremendously the need for annotated data. The disambiguation process is based on a graph-based label propagation method which takes into account mono or multimodal representations for pairs. Experiments have been carried out on the recently published dataset VerSe, the only available dataset for this task. The achieved results outperform the current state-of-the-art by a large margin while using only a small fraction of labeled samples per sens
Relaxation Labeling Meets GANs: Solving Jigsaw Puzzles with Missing Borders
No full textThis paper proposes JiGAN, a GAN-based method for solving Jigsaw puzzles with eroded or missing borders. Missing borders is a common real-world situation, for example, when dealing with the reconstruction of broken artifacts or ruined frescoes. In this particular condition, the puzzle’s pieces do not align perfectly due to the borders’ gaps; in this situation, the patches’ direct match is unfeasible due to the lack of color and line continuations. JiGAN, is a two-steps procedure that tackles this issue: first, we repair the eroded borders with a GAN-based image extension model and measure the alignment affinity between pieces; then, we solve the puzzle with the relaxation labeling algorithm to enforce consistency in pieces positioning, hence, reconstructing the puzzle. We test the method on a large dataset of small puzzles and on three commonly used benchmark datasets to demonstrate the feasibility of the proposed approach
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