Design Patent Retrieval Based on Gabor Wavelet and LBP

2015 ◽  
Vol 743 ◽  
pp. 503-509
Author(s):  
Q.Q. Li ◽  
C.S. Zhou ◽  
X.Q. Lv ◽  
H.Y. Yang ◽  
K. Zhang
Keyword(s):  
Image Retrieval ◽  
Gabor Filter ◽  
Texture Feature ◽  
Gabor Wavelet ◽  
Retrieval Method ◽  
Patent Retrieval ◽  
Retrieval Accuracy ◽  
Design Patent ◽  
Low Pass ◽  
Similar Images

Due to the diversity and complexity of design patent images, it is difficult to retrieve well if extracting features from images directly. A design patent image retrieval method based on Gabor filter and LBP is proposed in the paper. Firstly, doing low-pass filtering to the normalized images with Gabor filter to amplify the images’ details, then extracting image’s texture feature with LBP algorithm, calculating images’ similarity according to the distance formula after feature vectors’ internal normalization, finally return several similar images. The experimental results show that this retrieval method get better retrieval accuracy and correct rate.

A NOVEL TWO-LEVEL COLOR IMAGE RETRIEVAL METHOD

2011 ◽  
Vol 11 (03) ◽  
pp. 339-353 ◽  
Author(s):  
XING-YUAN WANG ◽  
ZHI-FENG CHEN ◽  
JIAO-JIAO YUN
Keyword(s):  
Image Retrieval ◽  
Digital Image ◽  
Color Image ◽  
Image Databases ◽  
Color Model ◽  
Retrieval Method ◽  
Retrieval Accuracy ◽  
Similar Images

This paper presents an effective two-level color image retrieval method based on the RGB color model. For the purpose of effectively retrieving more similar images from the digital image databases, we divide the image into different regions and set bigger weight for the region we focus on. In addition, we set different weights for each RGB component of the color image according to the main hue of it. As a result, this scheme can enhance the retrieval accuracy that is measured in terms of the recall and precision.

Ensemble Retrieval Method for Texture Image Based on Wavelet Transform

2011 ◽  
Vol 58-60 ◽  
pp. 274-279
Author(s):  
Ai Guo Li ◽  
Jun Bo Shao
Keyword(s):  
Wavelet Transform ◽  
Image Retrieval ◽  
Texture Feature ◽  
Texture Image ◽  
Multiple Methods ◽  
Retrieval Method ◽  
Retrieval Accuracy ◽  
Single Method ◽  
Intermediate Part

Texture feature of texture image is described by image energy. Wavelet transform is studied for image retrieval based on the texture image energy distributed in different components. An ensemble retrieval method is proposed, based on multiple methods fusion due to certain limitation of a single method. To improve the image retrieval accuracy, the main information of the image is highlighted by increasing the weight of the intermediate part. It proves that the method proposed in this paper and based on wavelet transform for texture image in ensemble retrieval has better retrieval effect.

scholarly journals Skin image retrieval using Gabor wavelet texture feature

2016 ◽  
Vol 38 (6) ◽  
pp. 607-614 ◽  
Author(s):  
X. Ou ◽  
W. Pan ◽  
X. Zhang ◽  
P. Xiao
Keyword(s):  
Image Retrieval ◽  
Texture Feature ◽  
Gabor Wavelet

A Content Based Image Retrieval Method Based on K-Means Clustering Technique

2018 ◽  
Vol 16 (1) ◽  
pp. 82-96 ◽  
Author(s):  
Mohamed Ouhda ◽  
Khalid El Asnaoui ◽  
Mohammed Ouanan ◽  
Brahim Aksasse
Keyword(s):  
Image Retrieval ◽  
Accurate Result ◽  
Efficient Algorithms ◽  
Image Databases ◽  
New Method ◽  
Content Based Image Retrieval ◽  
Computational Time ◽  
Retrieval Method ◽  
Clustering Technique ◽  
Similar Images

With the appearance of many devices that are used in image acquisition comes a large number of images every day. The rapid access to these huge collections of images and retrieval of similar images (Query) from this huge collection of images presents major challenges and requires efficient algorithms. The main goal of the proposed system is to provide an accurate result with lower computational time. For this purpose, the authors apply a new method based on k-means clustering technique to match image's descriptors. This article provides a detailed view of the solution the authors have adopted and which perfectly meets their needs. For validation, they apply all of these techniques on two image databases in order to evaluate the performance of their system.

scholarly journals Proposed a Content-Based Image Retrieval System Based on the Shape and Texture Features

2019 ◽  
Vol 8 (11) ◽  
pp. 2185-2192
Keyword(s):  
Image Retrieval ◽  
Texture Feature ◽  
Texture Features ◽  
Visual Image ◽  
Content Based Image Retrieval ◽  
Query Image ◽  
Feature Extraction Method ◽  
Image Retrieval System ◽  
Reference Images ◽  
Similar Images

In this paper, we proposed a fusion feature extraction method for content based image retrieval. The feature is extracted by focusing on the texture and shape features of the visual image by using the Local Binary Pattern (LBP – texture feature) and Edge Histogram Descriptor (EHD – shape feature). The SVD is used for decreasing the number of the feature vector of images. The Kd-tree is used for reducing the retrieval time. The input to this system is a query image and Database (the reference images) and the output is the top n most similar images for the query image. The proposed system is evaluated by using (precision and recall) to measure the retrieval effectiveness. The values of the recall are between [43% –93%] and the average recall is 64.3%. The values of precision are between [30%-100%] and the average is 72.86% for the entire system and for both databases

scholarly journals Hierarchical Image Retrieval by Multi-Feature Fusion

2017 ◽  
Author(s):  
Xiaojun Lu ◽  
Jiaojuan Wang ◽  
Yingqi Hou ◽  
Mei Yang ◽  
Qi Wang ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Large Time ◽  
Retrieval System ◽  
Feature Fusion ◽  
Retrieval Method ◽  
Time Consumption ◽  
Retrieval Accuracy ◽  
Single Feature ◽  
Image Retrieval System ◽  
Accuracy Rates

Aiming at the problems that are poor generalization performance, low retrieval accuracy and large time consumption of existing content-based image retrieval system, the hierarchical image retrieval method based on multi feature fusion is proposed in this paper. The retrieval accuracy rates on Corel5K, UKbeach and Holidays are 68.23(Top 1), 3.73(N-S) and 88.20(mAp), respectively. The experimental results show that the method proposed in this paper can effectively improve the deficiency of single feature retrieval and save time significantly in the premise of a small amount of loss of accuracy.

Rotation Invariant Texture Image Retrieval with Orthogonal Polynomials Model

2013 ◽  
pp. 239-261
Author(s):  
R. Krishnamoorthi ◽  
S. Sathiya Devi
Keyword(s):  
Image Retrieval ◽  
Orthogonal Polynomials ◽  
Distance Measure ◽  
Feature Vector ◽  
Gabor Filter ◽  
Computational Cost ◽  
Texture Feature ◽  
Image Data ◽  
Scale Invariant ◽  
Retrieval Technique

The exponential growth of digital image data has created a great demand for effective and efficient scheme and tools for browsing, indexing and retrieving images from a collection of large image databases. To address such a demand, this paper proposes a new content based image retrieval technique with orthogonal polynomials model. The proposed model extracts texture features that represent the dominant directions, gray level variations and frequency spectrum of the image under analysis and the resultant texture feature vector becomes rotation and scale invariant. A new distance measure in the frequency domain called Deansat is proposed as a similarity measure that uses the proposed feature vector for efficient image retrieval. The efficiency of the proposed retrieval technique is experimented with the standard Brodatz, USC-SIPI and VisTex databases and is compared with Discrete Cosine Transform (DCT), Tree Structured Wavelet Transform (TWT) and Gabor filter based retrieval schemes. The experimental results reveal that the proposed method outperforms well with less computational cost.

A Hierarchial Classification Technique for Semantics-Based Image Retrieval

2011 ◽  
pp. 311-333 ◽  
Author(s):  
Mohammed Lamine Kherfi ◽  
Djemel Ziou
Keyword(s):  
Image Retrieval ◽  
Retrieval Accuracy ◽  
The Third ◽  
Retrieval Systems ◽  
Probabilistic Classification ◽  
Semantic Concepts ◽  
Similar Images ◽  
Hierarchical Index ◽  
Upper Level ◽  
Abstraction Levels

We present a new approach for improving image retrieval accuracy by integrating semantic concepts. First, images are represented according to different abstraction levels. At the lowest level, they are represented with visual features. At the upper level, they are represented with a set of very specific keywords. At the subsequent levels, they are represented with more general keywords. Second, visual content together with keywords are used to create a hierarchical index. A probabilistic classification approach is proposed, which allows to group similar images into the same class. Finally this index is exploited to define three retrieval mechanisms: the first is text-based, the second is content-based, and the third is a combination of both. Experiments show that our combination allows to nicely narrow the semantic gap encountered by most current image retrieval systems. Furthermore, we show that the proposed method helps reducing retrieval time and improving retrieval accuracy.

Rotation Invariant Texture Image Retrieval with Orthogonal Polynomials Model

2011 ◽  
Vol 1 (4) ◽  
pp. 27-49
Author(s):  
R. Krishnamoorthi ◽  
S. Sathiya Devi
Keyword(s):  
Image Retrieval ◽  
Orthogonal Polynomials ◽  
Distance Measure ◽  
Feature Vector ◽  
Gabor Filter ◽  
Computational Cost ◽  
Texture Feature ◽  
Image Data ◽  
Scale Invariant ◽  
Retrieval Technique

The exponential growth of digital image data has created a great demand for effective and efficient scheme and tools for browsing, indexing and retrieving images from a collection of large image databases. To address such a demand, this paper proposes a new content based image retrieval technique with orthogonal polynomials model. The proposed model extracts texture features that represent the dominant directions, gray level variations and frequency spectrum of the image under analysis and the resultant texture feature vector becomes rotation and scale invariant. A new distance measure in the frequency domain called Deansat is proposed as a similarity measure that uses the proposed feature vector for efficient image retrieval. The efficiency of the proposed retrieval technique is experimented with the standard Brodatz, USC-SIPI and VisTex databases and is compared with Discrete Cosine Transform (DCT), Tree Structured Wavelet Transform (TWT) and Gabor filter based retrieval schemes. The experimental results reveal that the proposed method outperforms well with less computational cost.

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