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.

On-Line Chinese Character Recognition Via a Representation of Spatial Relationships Between Strokes

1997 ◽  
Vol 11 (03) ◽  
pp. 329-357 ◽  
Author(s):  
Ju-Wei Chen ◽  
Suh-Yin Lee
Keyword(s):  
Character Recognition ◽  
Chinese Character ◽  
Spatial Relationship ◽  
Spatial Relations ◽  
Chinese Characters ◽  
Spatial Relationships ◽  
Matching Problem ◽  
Structural Rules ◽  
Chinese Character Recognition ◽  
On Line

Chinese characters are constructed by basic strokes based on structural rules. In handwritten characters, the shapes of the strokes may vary to some extent, but the spatial relations and geometric configurations of the strokes are usually maintained. Therefore these spatial relations and configurations could be regarded as invariant features and could be used in the recognition of handwritten Chinese characters. In this paper, we investigate the structural knowledge in Chinese characters and propose the stroke spatial relationship representation (SSRR) to describe Chinese characters. An On-Line Chinese Character Recognition (OLCCR) method using the SSRR is also presented. With SSRR, each character is processed and is represented by an attribute graph. The process of character recognition is thereby transformed into a graph matching problem. After careful analysis, the basic spatial relationship between strokes can be characterized into five classes. A bitwise representation is adopted in the design of the data structure to reduce storage requirements and to speed up character matching. The strategy of hierarchical search in the preclassification improves the recognition speed. Basically, the attribute graph model is a generalized character representation that provides a useful and convenient representation for newly added characters in an OLCCR system with automatic learning capability. The significance of the structural approach of character recognition using spatial relationships is analyzed and is proved by experiments. Realistic testing is provided to show the effectiveness of the proposed method.

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.

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.

Design of the Ground Control Point Image Databases

2013 ◽  
Vol 427-429 ◽  
pp. 1493-1496
Author(s):  
Yan Li ◽  
Xiao He Zhang
Keyword(s):  
Control Point ◽  
Image Data ◽  
Image Database ◽  
Image Databases ◽  
Ground Control ◽  
Ground Control Point ◽  
Storage Solutions ◽  
Point Image ◽  
Point Data ◽  
Aerospace Images

Ground control point data is necessary in the aerospace images geometric processing. This study proposed the design solutions for the ground control point image databases. At first, this study compared the storage management manners of the control point data. Then, it analyzed the control point image data features. Accordingly, it mentioned the database storage solutions. Next, this study discussed the image control point database query methods. Finally, it designed a ground control point image database management and application system. The experiments show that it is an effective method for the control point image database.

scholarly journals Partitioning Medical Image Databases for Content-based Queries on a Grid

2005 ◽  
Vol 44 (02) ◽  
pp. 154-160 ◽  
Author(s):  
V. Breton ◽  
I. E. Magnin ◽  
J. Montagnat
Keyword(s):  
Theoretical Model ◽  
Image Retrieval ◽  
Medical Image ◽  
Computation Time ◽  
Image Database ◽  
Image Databases ◽  
Content Based Image Retrieval ◽  
Medical Databases ◽  
Grid Nodes ◽  
The Impact

Summary Objectives: In this paper we study the impact of executing a medical image database query application on the grid. For lowering the total computation time, the image database is partitioned into subsets to be processed on different grid nodes. Methods: A theoretical model of the application complexity and estimates of the grid execution overhead are used to efficiently partition the database. Results: We show results demonstrating that smart partitioning of the database can lead to significant improvements in terms of total computation time. Conclusions: Grids are promising for content-based image retrieval in medical databases.

An Approach for Hand Vein Representation and Indexing

2011 ◽  
Vol 3 (2) ◽  
pp. 1-15
Author(s):  
D S Guru ◽  
K B Nagasundara ◽  
S Manjunath ◽  
R Dinesh
Keyword(s):  
Spatial Relationship ◽  
Image Database ◽  
Spatial Relationships ◽  
Query Image ◽  
Indexing Structure ◽  
Representation Model ◽  
Hand Vein ◽  
Graph Properties ◽  
Junction Points

This paper proposes a model for representing and indexing of hand vein images. The proposed representation model identifies the junction points and perceives the spatial relationships existing among all junction points in hand vein images by the use of triangular spatial relationship (TSR). The model preserves the TSR among the junction points in a symbolic hand vein image by the use of quadruples and for each quadruple, a unique TSR key is generated. A novel methodology to label the junction points based on graph properties of junction points is also proposed. A Symbolic Hand Vein Image Database (SHVID) is created through the construction of B-tree, an efficient multilevel indexing structure. A methodology to retrieve similar symbolic hand vein images for a given query image is also presented. The proposed methodology has shown promising results.

scholarly journals Image-Text Joint Learning for Social Images with Spatial Relation Model

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Jiangfan Feng ◽  
Xuejun Fu ◽  
Yao Zhou ◽  
Yuling Zhu ◽  
Xiaobo Luo
Keyword(s):  
Large Scale ◽  
Clustering Algorithm ◽  
Semantic Analysis ◽  
Spatial Relationship ◽  
Spatial Relation ◽  
Spatial Relations ◽  
Image Features ◽  
Sensor Technology ◽  
Joint Learning ◽  
Social Images

The rapid developments in sensor technology and mobile devices bring a flourish of social images, and large-scale social images have attracted increasing attention to researchers. Existing approaches generally rely on recognizing object instances individually with geo-tags, visual patterns, etc. However, the social image represents a web of interconnected relations; these relations between entities carry semantic meaning and help a viewer differentiate between instances of a substance. This article forms the perspective of the spatial relationship to exploring the joint learning of social images. Precisely, the model consists of three parts: (a) a module for deep semantic understanding of images based on residual network (ResNet); (b) a deep semantic analysis module of text beyond traditional word bag methods; (c) a joint reasoning module from which the text weights obtained using image features on self-attention and a novel tree-based clustering algorithm. The experimental results demonstrate the effectiveness of using Flickr30k and Microsoft COCO datasets. Meanwhile, our method considers spatial relations while matching.

Regularized color clustering in medical image database

2000 ◽  
Vol 19 (11) ◽  
pp. 1150-1155 ◽  
Author(s):  
C.H. Li ◽  
P.C. Yuen
Keyword(s):  
Medical Image ◽  
Image Database ◽  
Color Clustering
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