DOI: 10.5281/zenodo.5878773
title: The Semantic PASCAL-Part Dataset

description: <strong>The Semantic PASCAL-Part dataset</strong> The Semantic PASCAL-Part dataset is the RDF version of the famous PASCAL-Part dataset used for object detection in Computer Vision. Each image is annotated with <strong>bounding boxes</strong> containing a single object. Couples of bounding boxes are annotated with the part-whole relationship. For example, the bounding box of a car has the part-whole annotation with the bounding boxes of its wheels. This original release joins Computer Vision with Semantic Web as the objects in the dataset are aligned with concepts from: the provided supporting ontology; the WordNet database through its synstes; the Yago ontology. The provided Python 3 code (see the GitHub repo) is able to browse the dataset and convert it in RDF knowledge graph format. This new format easily allows the fostering of research in both Semantic Web and Machine Learning fields. <strong>Structure of the semantic PASCAL-Part Dataset</strong> This is the folder structure of the dataset: <code>semanticPascalPart</code>: it contains the refined images and annotations (e.g., small specific parts are merged into bigger parts) of the PASCAL-Part dataset in Pascal-voc style. <code>Annotations_set</code>: the test set annotations in <code>.xml</code> format. For further information See the PASCAL VOC format here. <code>Annotations_trainval</code>: the train and validation set annotations in <code>.xml</code> format. For further information See the PASCAL VOC format here. <code>JPEGImages_test</code>: the test set images in <code>.jpg</code> format. <code>JPEGImages_trainval</code>: the train and validation set images in <code>.jpg</code> format. <code>test.txt</code>: the 2416 image filenames in the test set. <code>trainval.txt</code>: the 7687 image filenames in the train and validation set. <strong>The PASCAL-Part Ontology</strong> The PASCAL-Part OWL ontology formalizes, through logical axioms, the part-of relationship between whole objects (22 classes) and their parts (39 classes). The ontology contains 85 logical axiomns in Description Logic in (for example) the following form: <pre><code>Every potted_plant has exactly 1 plant AND has exactly 1 pot </code></pre> We provide two versions of the ontology: with and without cardinality constraints in order to allow users to experiment with or without them. The WordNet alignment is encoded in the ontology as annotations. We further provide the <code>WordNet_Yago_alignment.csv</code> file with both WordNet and Yago alignments. The ontology can be browsed with many Semantic Web tools such as: Protégé: a graphical tool for ongology modelling; OWLAPI: Java API for manipulating OWL ontologies; rdflib: Python API for working with the RDF format. RDF stores: databases for storing and semantically retrieve RDF triples. See here for some examples. <strong>Citing semantic PASCAL-Part</strong> If you use semantic PASCAL-Part in your research, please use the following BibTeX entry <pre><code>@article{DBLP:journals/ia/DonadelloS16, author = {Ivan Donadello and Luciano Serafini}, title = {Integration of numeric and symbolic information for semantic image interpretation}, journal = {Intelligenza Artificiale}, volume = {10}, number = {1}, pages = {33--47}, year = {2016} } </code></pre>