An Image Retrieval System Based on Automatic Image Annotation to Facilitate Digital Humanities Research

Existing image annotation approaches mainly concentrate on achieving annotation results. Annotation order has not been taken into account carefully. As orderly annotation list could enhance the performance of image retrieval system, it is of great importance to rank annotations. This paper presents an algorithm to rank Web image annotating results. For an annotated Web image, we firstly partition the image by a region growing method. Secondly, relevance degree between two annotations is estimated through considering both semantic similarity and image content. Next, the regions of unlabeled image to be ranked serve as queries and annotations are used as the data points to be ranked. And then, manifold-ranking algorithm is executed to get the ordered annotation list. Experiments conducted on real-world Web images through NDCG metric demonstrate the effectiveness of the proposed approach.

Download Full-text

Research on Constructing a Multi-Label Image Annotation and Retrieval System

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.20-23.559 ◽

2010 ◽

Vol 20-23 ◽

pp. 559-564

Author(s):

Yu Long Tian ◽

Ran Li ◽

Jian Jiang Lu ◽

Ya Fei Zhang ◽

Zi Ning Lu

Keyword(s):

Genetic Algorithm ◽

Image Retrieval ◽

High Precision ◽

Retrieval System ◽

Image Annotation ◽

Visual Features ◽

Semantic Level ◽

Optimal Weights ◽

Image Retrieval System ◽

Optimal Feature

In this paper, we design and construct a multi-label image annotation and retrieval system. Various MPEG-7 low level visual features are employed for representing images. For image annotation, a bi-coded genetic algorithm is employed to select optimal feature subsets and corresponding optimal weights for every one vs. one SVM classifiers. After an unlabelled image is segmented into several regions, pre-trained SVMs are used to annotate each region, final label is obtained by merging all the region labels. Based on multi-label of image, image retrieval system provides keyword-based image retrieval service. Multi-labels can provide abundant descriptions for image content in semantic level, high precision annotation algorithm further improve annotation performance.

Download Full-text

A Multi-Label Image Annotation With Multi-Level Tagging System

Developments and Trends in Intelligent Technologies and Smart Systems - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-3686-4.ch003 ◽

2018 ◽

pp. 28-47

Author(s):

Kalaivani Anbarasan ◽

Chitrakala S.

Keyword(s):

Image Retrieval ◽

Retrieval System ◽

Image Annotation ◽

Image Features ◽

Semantic Gap ◽

Low Level ◽

Image Retrieval System ◽

Multi Level ◽

High Level ◽

Tagging System

The content based image retrieval system retrieves relevant images based on image features. The lack of performance in the content based image retrieval system is due to the semantic gap. Image annotation is a solution to bridge the semantic gap between low-level content features and high-level semantic concepts Image annotation is defined as tagging images with a single or multiple keywords based on low-level image features. The major issue in building an effective annotation framework is the integration of both low level visual features and high-level textual information into an annotation model. This chapter focus on new statistical-based image annotation model towards semantic based image retrieval system. A multi-label image annotation with multi-level tagging system is introduced to annotate image regions with class labels and extract color, location and topological tags of segmented image regions. The proposed method produced encouraging results and the experimental results outperformed state-of-the-art methods

Download Full-text