A Human Retrieval System based on Human Attribute Ontology and Deep Multi-task Neural Network

The goal of this research is to enhance the capabilityof image retrieval systems to understand images moreeffectively. We present a model designed for searching humanobjects (such as pedestrians or persons) within expansiveimage datasets. Our unique approach involves developing animage retrieval system that incorporates attribute learningand the Human Attribute Ontology (HAO). This researchoffers several key contributions: (1) The development of theHuman Attribute Ontology (HAO) which serves as a repositoryfor storing prior knowledge about images. Thanks toits hierarchical structure, this ontology facilitates the reuse ofprior knowledge, optimizing the subsequent stages of attributelearning and image retrieval; (2) The implementation of aConvolutional Neural Network (CNN) to spearhead attributelearning, leveraging the HAO to enhance accuracy; (3) Thecreation of a Human Image Retrieval system that utilizes bothattribute learning and the HAO. Our system delves deeperby understanding images at the attribute level, highlightingthe advantages of harnessing the ontology to reuse existingknowledge. The efficacy of our methodology is validatedthrough experiments on benchmark datasets like PETA andPa100k achieving state-of-the-art results.