Intuitive Image Database Navigation by Hue-Sphere Browsing

2011 ◽  
pp. 263-270
Author(s):  
Gerald Schaefer ◽  
Simon Ruszala
Keyword(s):  
Image Database ◽  
Image Databases ◽  
Medium Size ◽  
Area Of Interest ◽  
Large Image Databases ◽  
Image Datasets

Efficient and effective techniques for managing and browsing large image databases are increasingly sought after. This chapter presents a simple yet efficient and effective approach to navigating image datasets. Based on the concept of a globe as visualisation and navigation medium, thumbnails are projected onto the surface of a sphere based on their colour. Navigation is performed by rotating and tilting the globe as well as zooming into an area of interest. Experiments based on a medium size image database demonstrate the usefulness of the presented approach.

An Efficient Content-Based Retrieval System for Large Image Database

2011 ◽  
pp. 249-276
Author(s):  
Ching-Sheng Wang ◽  
Timothy K. Shih
Keyword(s):  
Spatial Relationship ◽  
Image Database ◽  
Spatial Relations ◽  
Image Databases ◽  
Color Sensation ◽  
Spatial Features ◽  
Feedback Learning ◽  
Color Clustering ◽  
Object Similarity ◽  
Visual Interface

Content-based image retrieval has become more desirable for developing large image databases. This chapter presents an efficient method of retrieving images from an image database. This system combines color, shape and spatial features to index and measure the similarity of images. Several color spaces that are widely used in computer graphics are discussed and compared for color clustering. In addition, this chapter proposes a new automatic indexing scheme of image databases according to our clustering method and color sensation, which could be used to retrieve images efficiently. As a technical contribution, a Seed-Filling like algorithm that could extract the shape and spatial relationship feature of an image is proposed. Due to the difficulty of determining how far objects are separated, this system uses qualitative spatial relations to analyze object similarity. Also, the system is incorporated with a visual interface and a set of tools, which allows the users to express the query by specifying or sketching the images conveniently. The feedback learning mechanism enhances the precision of retrieval. The experience shows that the system is able to retrieve image information efficiently by the proposed approaches.

Effective and Efficient Browsing of Large Image Databases

2009 ◽  
pp. 142-148
Author(s):  
Gerald Schaefer
Keyword(s):  
Spherical Surface ◽  
Image Databases ◽  
Tree Structure ◽  
Retrieval Method ◽  
Hierarchical Tree ◽  
Large Databases ◽  
The Moment ◽  
Hierarchical Tree Structure ◽  
Large Image Databases ◽  
Interesting Alternative

As image databases are growing, efficient and effective methods for managing such large collections are highly sought after. Content-based approaches have shown large potential in this area as they do not require textual annotation of images. However, while for image databases the query-by-example concept is at the moment the most commonly adopted retrieval method, it is only of limited practical use. Techniques which allow human-centred navigation and visualization of complete image collections therefore provide an interesting alternative. In this chapter we present an effective and efficient approach for user-centred navigation of large image databases. Image thumbnails are projected onto a spherical surface so that images that are visually similar are located close to each other in the visualization space. To avoid overlapping and occlusion effects images are placed on a regular grid structure while large databases are handled through a clustering technique paired with a hierarchical tree structure which allows for intuitive real-time browsing experience.

Simulation of Fast Retrieval Method for Large-Scale Image Database

2014 ◽  
Vol 556-562 ◽  
pp. 4959-4962
Author(s):  
Sai Qiao
Keyword(s):  
Feature Extraction ◽  
Large Scale ◽  
Great Increase ◽  
Texture Features ◽  
Image Database ◽  
Image Databases ◽  
Matching Method ◽  
Retrieval Method ◽  
Database Retrieval ◽  
Database Constraints

The traditional database information retrieval method is achieved by retrieving simple corresponding association of the attributes, which has the necessary requirement that image only have a single characteristic, with increasing complexity of image, it is difficult to process further feature extraction for the image, resulting in great increase of time consumed by large-scale image database retrieval. A fast retrieval method for large-scale image databases is proposed. Texture features are extracted in the database to support retrieval in database. Constraints matching method is introduced, in large-scale image database, referring to the texture features of image in the database to complete the target retrieval. The experimental results show that the proposed algorithm applied in the large-scale image database retrieval, augments retrieval speed, thereby improves the performance of large-scale image database.

scholarly journals Match Graph Construction for Large Image Databases

2012 ◽  
pp. 272-285 ◽  
Author(s):  
Kwang In Kim ◽  
James Tompkin ◽  
Martin Theobald ◽  
Jan Kautz ◽  
Christian Theobalt
Keyword(s):  
Image Databases ◽  
Large Image Databases

Hierarchical browsing and search of large image databases

2000 ◽  
Vol 9 (3) ◽  
pp. 442-455 ◽  
Author(s):  
Jau-Yuen Chen ◽  
C.A. Bouman ◽  
J.C. Dalton
Keyword(s):  
Image Databases ◽  
Large Image Databases
Sign in / Sign up

Export Citation Format

Share Document