The Semantic PASCAL-Part Dataset

<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>**语义PASCAL-Part数据集（Semantic PASCAL-Part dataset）** 语义PASCAL-Part数据集是知名的、原本用于计算机视觉目标检测任务的PASCAL-Part数据集的资源描述框架（RDF）版本。该数据集中的每张图像均通过**边界框（bounding boxes）**标注单个目标，而成对的边界框则会被标注部分-整体关系。例如，汽车的边界框与其车轮的边界框之间就带有该部分-整体标注。 本次发布实现了计算机视觉与语义网技术的融合，因为数据集中的目标与三类概念实现了对齐：本次提供的支撑本体、通过同义词集对齐的WordNet数据库，以及Yago本体。 本次提供的Python 3代码（详见GitHub仓库）可用于浏览该数据集，并将其转换为RDF知识图谱格式，这一新格式能够有效推动语义网与机器学习领域的相关研究。 **语义PASCAL-Part数据集的组织结构** 该数据集的文件夹结构如下： - `semanticPascalPart`：包含PASCAL-Part数据集经精炼处理的图像与标注（例如将小型特定部件合并为更大部件），采用Pascal-voc格式。 - `Annotations_set`：测试集标注文件，格式为`.xml`，更多PASCAL VOC格式的细节详见此处说明。 - `Annotations_trainval`：训练与验证集标注文件，格式为`.xml`，更多PASCAL VOC格式的细节详见此处说明。 - `JPEGImages_test`：测试集图像文件，格式为`.jpg`。 - `JPEGImages_trainval`：训练与验证集图像文件，格式为`.jpg`。 - `test.txt`：包含测试集的2416个图像文件名。 - `trainval.txt`：包含训练与验证集的7687个图像文件名。 **PASCAL-Part本体** PASCAL-Part 网络本体语言（OWL）本体通过逻辑公理对整体目标（共22个类）与其部件（共39个类）之间的部分-整体关系进行了形式化定义。该本体包含85条描述逻辑（Description Logic）形式的逻辑公理，例如： Every potted_plant has exactly 1 plant AND has exactly 1 pot （可译为：所有盆栽植物恰好拥有1个植株且恰好拥有1个花盆） 本次提供了该本体的两个版本：分别带有基数约束与不带基数约束，以支持用户按需开展相关实验。本体中的WordNet对齐关系以注释形式进行编码，此外还额外提供了`WordNet_Yago_alignment.csv`文件，其中包含了WordNet与Yago的对齐信息。 该本体可通过多款语义网工具进行浏览与操作，例如： 1. Protégé：用于本体建模的图形化工具； 2. OWLAPI：用于操作OWL本体的Java API； 3. rdflib：用于处理RDF格式数据的Python API； 以及RDF存储库：用于存储并语义检索RDF三元组的数据库，相关示例详见此处。 **引用语义PASCAL-Part数据集** 若您在研究工作中使用语义PASCAL-Part数据集，请使用以下BibTeX引用条目： bibtex @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} }