Image retrieval of wool fabric. Part I: Based on low-level texture features

2019 ◽  
Vol 89 (19-20) ◽  
pp. 4195-4207 ◽  
Author(s):  
Ning Zhang ◽  
Jun Xiang ◽  
Lei Wang ◽  
Weidong Gao ◽  
Ruru Pan
Keyword(s):  
Fourier Transform ◽  
Image Retrieval ◽  
Frequency Spectrum ◽  
Local Binary Pattern ◽  
Texture Features ◽  
Histogram Equalization ◽  
Wool Fabric ◽  
Image Histogram ◽  
Retrieval Efficiency ◽  
Local Binary Pattern Feature

With huge and ever-growing products in the factory, image retrieval can help the worker retrieve the same, or similar, existing products rapidly and accurately to guide production. In this paper, an effective method based on Fourier transform and local binary pattern is proposed to improve the retrieval efficiency of wool fabric. After capturing the fabric image, histogram equalization was implemented on the value of the Hue, Saturation, Value (HSV) mode to enhance the contrast. Subsequently, Fourier transform together with local binary pattern operator were performed to obtain the frequency spectrum and the local binary pattern, respectively. Each frequency spectrum was divided into 22 rings with the same width, and the standard deviation of the frequencies in each ring was calculated as a Fourier feature. Distinct output values of each local binary pattern were counted and normalized as local binary pattern features. Finally, Euclidean distance was adopted to measure the similarity based on the Fourier feature and local binary pattern feature. Twenty thousand wool fabric images were captured to demonstrate the efficacy of the proposed method. Experimental results indicate that the framework is effective and superior for image retrieval of wool fabric, providing referential assistance for the worker in the factory and improving retrieval efficiency.

scholarly journals Texton Based Image Retrieval Using Indexed LBP Transitions

2014 ◽  
Vol 13 (12) ◽  
pp. 5286-5300 ◽  
Author(s):  
A. Srinivasa Rao ◽  
V.Venkata Krishna ◽  
Prof.YKSundara Krishna
Keyword(s):  
Image Retrieval ◽  
Data Base ◽  
Local Binary Pattern ◽  
Texture Features ◽  
Experimental Result ◽  
Texture Image ◽  
Huge Data ◽  
Proposed Model ◽  
Occurrence Matrix ◽  
Rotationally Invariant

The present paper derived a new model of texture image retrieval by integrating the transitions on Local Binary Pattern (LBP) with textons and Grey Level Co-occurrence Matrix (GLCM). The present paper initially derived transitions that occur from 0 to 1 or 1 to 0 in circular manner on LBP. The transitions reduce the 256 LBP codes into five texture features. This reduces the lot of complexity. The LBP codes are rotationally variant. The proposed circular transitions on LBP are rotationally invariant. Textons,which represents the local relationships,are detected on this. The GLCM features are evaluated on the texton based image for efficient image retrieval. The proposed method is experimented on a huge data base of textures collected from Google data base. The experimental result indicates the efficiency of the proposed model.

scholarly journals USING TEXTURE AND SHAPE FEATURES TO RETRIEVE SETS OF SIMILAR MEDICAL IMAGES

2003 ◽  
Vol 15 (05) ◽  
pp. 193-199 ◽  
Author(s):  
JIANN-DER LEE ◽  
LI-PENG LOU
Keyword(s):  
Standard Deviation ◽  
Image Retrieval ◽  
Wavelet Decomposition ◽  
Texture Features ◽  
Binary Codes ◽  
Shape Features ◽  
Retrieval Task ◽  
Texture Information ◽  
Retrieval Efficiency ◽  
The Mean

In this paper, a novel scheme has been proposed for image retrieval task using the feature extracted directly from a compressed or uncompressed image. The texture information is first extracted by exploiting the multiresolution nature of wavelet decomposition, which represent the horizontal, vertical and diagonal frequency distribution of an image. We then calculate the mean and standard deviation of wavelet coefficients of each sub-band as texture features. In additions, we also extract shape feature by using the fixed-resolution block representation, which divides the image into isometric blocks and calculate the overlapped degree of each block with binary codes. The experimental results show that the retrieval efficiency is considerably improved by the proposed approach.

scholarly journals Image Retrieval Model Analysis of Digital Library Based on Texture Characteristics

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yu Zhao
Keyword(s):  
Image Retrieval ◽  
Digital Library ◽  
Texture Features ◽  
Document Image ◽  
Retrieval Algorithm ◽  
Retrieval Model ◽  
Edge Information ◽  
Texture Characteristics ◽  
Retrieval Efficiency ◽  
Efficient Retrieval

A new document image retrieval algorithm is proposed in view of the inefficient retrieval of information resources in a digital library. First of all, in order to accurately characterize the texture and enhance the ability of image differentiation, this paper proposes the statistical feature method of the double-tree complex wavelet. Secondly, according to the statistical characteristic method, combined with the visual characteristics of the human eye, the edge information in the document image is extracted. On this basis, we construct the meaningful texture features and use texture features to define the characteristic descriptors of document images. Taking the descriptor as the clue, the content characteristics of the document image are combined organically, and appropriate similarity measurement criteria are used for efficient retrieval. Experimental results show that the algorithm not only has high retrieval efficiency but also reduces the complexity of the traditional document image retrieval algorithm.

scholarly journals Image Retrieval with Fusion of Thepade’s Sorted Block Truncation Coding n-ary based Color and Local Binary Pattern based Texture Features with Different Color Places

2020 ◽  
Vol 9 (5) ◽  
pp. 28-34
Keyword(s):  
Image Retrieval ◽  
Local Binary Pattern ◽  
Feature Vector ◽  
Color Space ◽  
Texture Features ◽  
Local Binary Patterns ◽  
Block Truncation Coding ◽  
Local Texture ◽  
Color Features ◽  
Different Color

In these years, there has been a gigantic growth in the generation of data. Innovations such as the Internet, social media and smart phones are the facilitators of this information boom. Since ancient times images were treated as an effective mode of communication. Even today most of the data generated is image data. The technology for capturing, storing and transferring images is well developed but efficient image retrieval is still a primitive area of research. Content Based Image Retrieval (CBIR) is one such area where lot of research is still going on. CBIR systems rely on three aspects of the image content namely texture, shape and color. Application specific CBIR systems are effective whereas Generic CBIR systems are being explored. Previously, descriptors are used to extract shape, color or texture content features, but the effect of using more than one descriptor is under research and may yield better results. The paper presents the fusion of TSBTC n-ary (Thepade's Sorted n-ary Block Truncation Coding) Global Color Features and Local Binary Pattern (LBP) Local Texture Features in Content Based Image with Different Color Places TSBTC n-ary devises global color features from an image. It is a faster and better technique compared to Block Truncation Coding. It is also rotation and scale invariant. When applied on an image TSBTC n-ary gives a feature vector based on the color space, if TSBTC n-ary is applied on the obtained LBP (Local Binary Patterns) of the image color planes, the feature vector obtained is be based on local texture content. Along with RGB, the Luminance chromaticity color space like YCbCr and Kekre’s LUV are also used in experimentation of proposed CBIR techniques. Wang dataset has been used for exploration of proposed method. It consists of 1000 images (10 categories having 100 images each). Obtained results have shown performance improvement using fusion of BTC extracted global color features and local texture features extracted with TSBTC n-ary applied on Local Binary Patterns (LBP).

Content-based image retrieval using local texture features in distributed environment

2019 ◽  
Vol 18 (01) ◽  
pp. 1941001
Author(s):  
U. S. N. Raju ◽  
K. Suresh Kumar ◽  
Pulkesh Haran ◽  
Ramya Sree Boppana ◽  
Niraj Kumar
Keyword(s):  
Image Retrieval ◽  
Local Binary Pattern ◽  
Feature Vector ◽  
Texture Features ◽  
Content Based Image Retrieval ◽  
Distributed Environment ◽  
Average Precision ◽  
Neighboring Pixel ◽  
Hadoop Cluster ◽  
The Relationship

In this paper, we propose novel content-based image retrieval (CBIR) algorithms using Local Octa Patterns (LOtP), Local Hexadeca Patterns (LHdP) and Direction Encoded Local Binary Pattern (DELBP). LOtP and LHdP encode the relationship between center pixel and its neighbors based on the pixels’ direction obtained by considering the horizontal, vertical and diagonal pixels for derivative calculations. In DELBP, direction of a referenced pixel is determined by considering every neighboring pixel for derivative calculations which results in 256 directions. For this resultant direction encoded image, we have obtained LBP which is considered as feature vector. The proposed method’s performance is compared to that of Local Tetra Patterns (LTrP) using benchmark image databases viz., Corel 1000 (DB1) and Brodatz textures (DB2). Performance analysis shows that LOtP improves the average precision from 59.31% to 64.36% on DB1, and from 83.24% to 85.95% on DB2, LHdP improves it to 65.82% on DB1 and to 87.49% on DB2 and DELBP improves it to 60.35% on DB1 and to 86.12% on DB2 as compared to that of LTrP. Also, DELBP reduces the feature vector length by 66.62% as compared to that of LTrP. To reduce the retrieval time, the proposed algorithms are implemented on a Hadoop cluster consisting of 116 nodes and tested using Corel 10K (DB3), Mirflickr 100,000 (DB4) and ImageNet 511,380 (DB5) databases.

An Enhancement of Multi Classifiers Voting Method for Mammogram Image based on Image Histogram Equalization

2018 ◽  
Vol 10 (6) ◽  
Author(s):  
Ashraf Osman Ibrahim ◽  
◽  
Ali Ahmed ◽  
Anik Hanifatul Azizah ◽  
Saima Anwar Lashar ◽  
...  
Keyword(s):  
Histogram Equalization ◽  
Image Histogram ◽  
Mammogram Image ◽  
Voting Method

Image Retrieval Based on Regional Color-Texture Features Description and DPF Matching

2011 ◽  
Vol 30 (5) ◽  
pp. 1113-1117
Author(s):  
Gui-ting Wang ◽  
Zhi-fang Guo ◽  
Li-cheng Jiao
Keyword(s):  
Image Retrieval ◽  
Texture Features ◽  
Color Texture

Image Retrieval using Modified Multi Texton and Rotation Invariant Local Binary Pattern

2020 ◽  
Author(s):  
Fitri Bimantoro ◽  
Ashri Annisaak Aziz ◽  
Ario Yudo Husodo ◽  
Ahmad Musnansyah ◽  
Agus Eko Minarno ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Local Binary Pattern ◽  
Rotation Invariant
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