SHAPE-BASED IMAGE RETRIEVAL APPLIED TO TRADEMARK IMAGES

2002 ◽  
Vol 02 (03) ◽  
pp. 375-393 ◽  
Author(s):  
OSSAMA EL BADAWY ◽  
MOHAMED KAMEL
Keyword(s):  
Feature Vector ◽  
Connected Components ◽  
Moment Invariants ◽  
Shape Features ◽  
Partial Match ◽  
Query Image ◽  
Retrieval Method ◽  
Database Retrieval ◽  
Trademark Images ◽  
The Moment

We propose a new shape-based, query-by-example, image database retrieval method that is able to match a query image to one of the images in the database, based on a whole or partial match. The proposed method has two key components: the architecture of the retrieval and the features used. Both play a role in the overall retrieval efficacy. The proposed architecture is based on the analysis of connected components and holes in the query and database images. The features we propose to use are geometric in nature, and are invariant to translation, rotation and scale. Each of the suggested three features is not new per se, but combining them to produce a compact and efficient feature vector is. We use hand-sketched, rotated and scaled query images to test the proposed method using a database of 500 logo images. We compare the performance of the suggested features with the performance of the moment invariants (a set of commonly-used shape features). The suggested features match the moment invariants in rotated and scaled queries and consistently surpass them in hand-sketched queries. Moreover, results clearly show that the proposed architecture significantly increases the performance of the two feature sets.

scholarly journals Color-Based Image Retrieval Using Proximity Space Theory

Algorithms ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 115 ◽  
Author(s):  
Jing Wang ◽  
Lidong Wang ◽  
Xiaodong Liu ◽  
Yan Ren ◽  
Ye Yuan
Keyword(s):  
Image Retrieval ◽  
Image Similarity ◽  
Space Theory ◽  
Query Image ◽  
Proximity Space ◽  
Retrieval Method ◽  
Color Features ◽  
Granule Structure ◽  
Structure Similarity ◽  
Similarity Method

The goal of object retrieval is to rank a set of images by their similarity compared with a query image. Nowadays, content-based image retrieval is a hot research topic, and color features play an important role in this procedure. However, it is important to establish a measure of image similarity in advance. The innovation point of this paper lies in the following. Firstly, the idea of the proximity space theory is utilized to retrieve the relevant images between the query image and images of database, and we use the color histogram of an image to obtain the Top-ranked colors, which can be regard as the object set. Secondly, the similarity is calculated based on an improved dominance granule structure similarity method. Thus, we propose a color-based image retrieval method by using proximity space theory. To detect the feasibility of this method, we conducted an experiment on COIL-20 image database and Corel-1000 database. Experimental results demonstrate the effectiveness of the proposed framework and its applications.

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.

scholarly journals CBIR Using Slant Transform Using DC & AC Coefficients

2018 ◽  
Vol 7 (3.6) ◽  
pp. 276 ◽  
Author(s):  
N Sravani ◽  
K Veera Swamy
Keyword(s):  
Data Base ◽  
Feature Vector ◽  
Image Database ◽  
Content Based Image Retrieval ◽  
Vector Data ◽  
Query Image ◽  
Retrieval Technique ◽  
Slant Transform ◽  
Measuring Techniques ◽  
The Difference

In the CBIR- (Content Based Image Retrieval) technique requires low-level or primitive features- color, texture, or  other data that can be taken from its image Extracting feature vectors of database images as well as query image can be calculated with the help of slant transform by considering DC & 3 AC coefficients obtained in each block of an image. Slant transform represents the gradual brightness changes in an image line effectively. By calculating the difference between feature vector data base and feature vector for a query by using the distance measuring techniques. The vector of the smaller distance is the closest to query image. The experiment is performed in the Corel 500 Image Database. Finally, CBIR results are evaluated by the recall, precision, and F-Score.  

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 Binary Plane Technique Based Color Quantization for Content Based Image Retrieval

2018 ◽  
Vol 7 (3.1) ◽  
pp. 124
Author(s):  
T Esther Ratna ◽  
N Subash Chandra
Keyword(s):  
Feature Vector ◽  
High Volume ◽  
Color Histogram ◽  
Color Quantization ◽  
Retrieval Method ◽  
It Value ◽  
Bit Plane ◽  
Plane Technique ◽  
Result Analysis ◽  
Precision Rate

Extracting accurate informative file from a high volume of graphic files is a challenging task. This paper focus on presenting a new color indexing approach                using the histogram features. Two histogram features like maximum color histogram and minimum color histogram are computed and are vector quantized to constitute a feature vector. Bit plane technique is used to map these features based upon it value at the respective position. The ultimate goal of any retrieval method is to attain higher precision within a short span of time that could be achieved if the data is in compressed to accomplish this the image is compressed using binary plane technique. The result analysis depicts the performance of the proposed approach under lossy and lossless modes and found that when operated in lossy it attain effective precision rate in a speculated amount of time. 

Vision-based force measurement using geometric moment invariants

2012 ◽  
Vol 226 (10) ◽  
pp. 2589-2601
Author(s):  
Wen Hu ◽  
Shigang Wang ◽  
Chun Hu ◽  
Hongtao Liu ◽  
Jinqiu Mo
Keyword(s):  
Measurement Method ◽  
Feature Vector ◽  
Force Measurement ◽  
Shape Descriptor ◽  
Support Vector ◽  
Moment Invariants ◽  
Geometric Moment ◽  
New Vision ◽  
Multi Class Classification ◽  
Standard Library

This article presents a new vision-based force measurement method to measure microassembly forces without directly computing the deformation. The shape descriptor of geometric moment invariants is used as a feature vector to describe the implicit relationship between an applied force and the deformation. Then, a standard library is established to map the corresponding relationship between the deformed cantilever under known forces and a set of feature vectors. Finally, a support vector machine compares the feature vector of deformed cantilever under an unknown force with those in the standard library, implements multi-class classification and predicts the unknown force. The vision-based force measurement method is validated for eight simulated microcantilevers of different sizes. Both regional and boundary moment invariants are used to constitute the feature vector. Simulated results show that the force measurement precision varies with length, width and height of cantilevers. If length increases and width and height decrease, the precision is higher. This trend can provide a reference for mechanism design of microcantilevers and microgrippers.

An effective hyperspectral image retrieval method using integrated spectral and textural features

Sensor Review ◽  
2015 ◽  
Vol 35 (3) ◽  
pp. 274-281 ◽  
Author(s):  
Zhenfeng Shao ◽  
Weixun Zhou ◽  
Qimin Cheng ◽  
Chunyuan Diao ◽  
Lei Zhang
Keyword(s):  
Design Methodology ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Hyperspectral Data ◽  
Textural Features ◽  
Washington Dc ◽  
Query Image ◽  
Retrieval Method ◽  
Content Type ◽  
Similar Images

Purpose – The purpose of this paper is to improve the retrieval results of hyperspectral image by integrating both spectral and textural features. For this purpose, an improved multiscale opponent representation for hyperspectral texture is proposed to represent the spatial information of the hyperspectral scene. Design/methodology/approach – In the presented approach, end-member signatures are extracted as spectral features by means of the widely used end-member induction algorithm N-FINDR, and the improved multiscale opponent representation is extracted from the first three principal components of the hyperspectral data based on Gabor filters. Then, the combination similarity between query image and other images in the database is calculated, and the first k more similar images are returned in descending order of the combination similarity. Findings – Some experiments are calculated using the airborne hyperspectral data of Washington DC Mall. According to the experimental results, the proposed method improves the retrieval results, especially for image categories that have regular textural structures. Originality/value – The paper presents an effective retrieval method for hyperspectral images.

Research and Implement the Image Retrieval Method Based on the Shape Feature

2011 ◽  
Vol 301-303 ◽  
pp. 1048-1051
Author(s):  
Xiao Juan Guo ◽  
Quan Rui Wang ◽  
Chang Jiang Li ◽  
Yun Juan Liang
Keyword(s):  
Image Retrieval ◽  
Image Database ◽  
Features Extraction ◽  
Shape Feature ◽  
Shape Features ◽  
Distance Metric ◽  
Retrieval Method ◽  
Similarity Metric

The paper has research and analyzed the arithmetic of shape features extraction and similarity metric, and adopted the Euclid distance metric, and according to the image database of the bird which from UIUC and the database of chair, strawberry from Caltech 101, the Hu invariants moments features extraction are validated. Compared these experiment results, at the different database, some image which has simple background and different shape object can be obtained the better retrieval effect.

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