*Improving Natural Language Queries Search and Retrieval through Semantic Image Annotation Understanding

Abstract

Retrieving images using detailed natural language queries remains a difficult challenge. Traditional annotation-based image retrieval systems using word matching techniques cannot efficiently support such query types. Significant improvements for this problem can be achieved with a semantic understanding for those query sentences and image annotations. This paper presents a two-stage semantic understanding approach for natural language query sentences. At the first stage, the Stanford parser and a designed rule-based relation extraction tool are used in triple extraction process to efficiently extract the objects attributes, instances and natural language annotations relationships involving these objects. The second stage integrates the extracted relations with external commonsense knowledge source in a mapping process to provide high-level sematic meanings to the extracted triples. Experiments are conducted for evaluating the benefit of the proposed semantic understanding against a testing set of natural language sentences from the Flickr8k dataset. The results show that the proposed approach succeeds to extract relational triples with average accuracy value of 97% for the different types of annotations relationships: attributes and instance relations, multiword dependence relations, and semantic relations.

Toward real-time image annotation using marginalized coupled dictionary learning

“…Needing to compute the similarity of a query image to all images in the dataset, search-based methods are inherently time-consuming which emphasizes the importance of introducing scalable methods. Regardless of mete-data-based approaches [27,31], different methods have been suggested for scalable image annotation, which can be categorized into three main groups, including prototype-based [28], dimensionality-reductionbased [11,21], and transform-based methods [15,44].…”