Semantic Image Annotation using Ontology And SPARQL

Based on user’s interest or requirements, the search and retrieve images from large scale the databases, the contentbased image retrieval (CBIR) technique has become the primary emerging area in research for digital image processing which makes the visual contents to use. Most promising tools for image searching are Google Images and Yahoo Image search. They are used for annotations based on textual of the images. In this, the images are annotated manually with the help of keywords and then the retrieval is carried by using various search methods based on text. Due to this method, the system performance is too low. Therefore, CBIR goal is to construct Image Ontology. The Ontology extracts the relevant images from the database by using low-level features like texture, shape and color. In multimedia technology, the challenging task is to retrieve the relevant images from an image database. For representation, organization and retrieving of images, the searching approaches based on semantic provide effective and efficient results by using image ontology. In this paper, protege software shows us how to create ontology and SPARQL query language provides semantic annotation for images. In addition to this, OntoViz and OntoGraph were used to generate Ontology in a graphical form for the relevant application

Semantic image annotation using convolutional neural network and wordnet ontology

Images are a major source of content on the web. The increase in mobile phones and digital cameras have led to huge amount of non-textual data being generated which is mostly images. Accurate annotation is critical for efficient image search and retrieval. Semantic image annotation refers to adding meaningful meta-data to an image which can be used to infer additional knowledge from an image. It enables users to perform complex queries and retrieve accurate image results. This paper proposes an image annotation technique that uses deep learning and semantic labeling. A convolutional neural network is used to classify images and the predicted class labels are mapped to semantic concepts. The results shows that combining semantic class labeling with image classification can help in polishing the results and finding common concepts and themes.

Review: Automatic Semantic Image Annotation

There are many approaches for automatic annotation in digital images. Nowadays digital photography is a common technology for capturing and archiving images because of the digital cameras and storage devices reasonable price. As amount of the digital images increase, the problem of annotating a specific image becomes a critical issue. Automated image annotation is creating a model capable of assigning terms to an image in order to describe its content. There are many image annotation techniques that seek to find the correlation between words and image features such as color, shape, and texture to provide an automatically correct annotation words to images which provides an alternative to the time consuming work of manual image annotation. This paper aims to cover a review on different Models (MT, CRM, CSD-Prop, SVD-COS and CSD-SVD) for automating the process of image annotation as an intermediate step in image retrieval process using Corel 5k images.