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Article

Chia-Ching Hung

The purpose of this study is to build a database of digital Chinese painting images and use the proposed technique to extract image and texture information, and search…

Abstract

Purpose

The purpose of this study is to build a database of digital Chinese painting images and use the proposed technique to extract image and texture information, and search images similar to the query image based on colour histogram and texture features in the database. Thus, retrieving images by this image technique is expected to make the retrieval of Chinese painting images more precise and convenient for users.

Design/methodology/approach

In this study, a technique is proposed that considers spatial information of colours in addition to texture feature in image retrieval. This technique can be applied to retrieval of Chinese painting images. A database of 1,200 digital Chinese painting images in three categories was built, including landscape, flower and figure. The authors develop an image-retrieval technique that considers colour distribution, spatial information of colours and texture.

Findings

In this study, a database of 1,200 digital Chinese painting images in three categories was built, including landscape, flower and figure. An image-retrieval technique was developed that considers colour distribution, spatial information of colours and texture. Through adjustment of feature values, this technique is able to process both landscape and portrait images. This technique also addresses liubai (i.e. blank) and text problems in the images. The experimental results confirm high precision rate of the proposed retrieval technique.

Originality/value

In this paper, a novel Chinese painting image-retrieval technique is proposed. Existing image-retrieval techniques and the features of Chinese painting are used to retrieve Chinese painting images. The proposed technique can exclude less important image information in Chinese painting images for instance liubai and calligraphy while calculating the feature values in them. The experimental results confirm that the proposed technique delivers a retrieval precision rate as high as 92 per cent and does not require a considerable computing power for feature extraction. This technique can be applied to Web page image retrieval or to other mobile applications.

Details

The Electronic Library, vol. 36 no. 1
Type: Research Article
ISSN: 0264-0473

Keywords

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Article

Neda Tadi Bani and Shervan Fekri-Ershad

Large amount of data are stored in image format. Image retrieval from bulk databases has become a hot research topic. An alternative method for efficient image retrieval…

Abstract

Purpose

Large amount of data are stored in image format. Image retrieval from bulk databases has become a hot research topic. An alternative method for efficient image retrieval is proposed based on a combination of texture and colour information. The main purpose of this paper is to propose a new content based image retrieval approach using combination of color and texture information in spatial and transform domains jointly.

Design/methodology/approach

Various methods are provided for image retrieval, which try to extract the image contents based on texture, colour and shape. The proposed image retrieval method extracts global and local texture and colour information in two spatial and frequency domains. In this way, image is filtered by Gaussian filter, then co-occurrence matrices are made in different directions and the statistical features are extracted. The purpose of this phase is to extract noise-resistant local textures. Then the quantised histogram is produced to extract global colour information in the spatial domain. Also, Gabor filter banks are used to extract local texture features in the frequency domain. After concatenating the extracted features and using the normalised Euclidean criterion, retrieval is performed.

Findings

The performance of the proposed method is evaluated based on the precision, recall and run time measures on the Simplicity database. It is compared with many efficient methods of this field. The comparison results showed that the proposed method provides higher precision than many existing methods.

Originality/value

The comparison results showed that the proposed method provides higher precision than many existing methods. Rotation invariant, scale invariant and low sensitivity to noise are some advantages of the proposed method. The run time of the proposed method is within the usual time frame of algorithms in this domain, which indicates that the proposed method can be used online.

Details

The Electronic Library , vol. 37 no. 4
Type: Research Article
ISSN: 0264-0473

Keywords

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Article

Qiao Sun, Shengxiu Zhang, Lijia Cao, Xiaofeng Li and Naixin Qi

The purpose of this paper is to improve the robustness of the traditional Bhattacharyya metric for the effect of histogram quantization in the histogram-based visual…

Abstract

Purpose

The purpose of this paper is to improve the robustness of the traditional Bhattacharyya metric for the effect of histogram quantization in the histogram-based visual tracking. However, the traditional Bhattacharyya metric neglects the correlation of crossing-bin and is not robust for the effect of histogram quantization.

Design/methodology/approach

In this paper, the authors propose a visual tracking method via crossing-bin histogram Bhattacharyya similarity in the particle filter.

Findings

A crossing-bin matrix is introduced into the traditional Bhattacharyya similarity for measuring the reference histogram and the candidate histogram, and the basic tasks of measure such as maximum similarity of self and the triangle inequality are proven. The authors use the proposed measure in the particle filter visual tracking framework and address a model update strategy based on the crossing-bin histogram Bhattacharyya similarity to improve the robustness of visual tracking.

Originality/value

In the experiments using the famous challenging benchmark sequences, precision of the proposed method increases by 12.8 per cent comparing the traditional Bhattacharyya similarity and the cost time decreases by 38 times comparing the incremental Bhattacharyya similarity. The experimental results show that the proposed method can track the object robustly and rapidly under illumination change and occlusion.

Details

Sensor Review, vol. 37 no. 4
Type: Research Article
ISSN: 0260-2288

Keywords

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Article

Shenlong Wang, Kaixin Han and Jiafeng Jin

In the past few decades, the content-based image retrieval (CBIR), which focuses on the exploration of image feature extraction methods, has been widely investigated. The…

Abstract

Purpose

In the past few decades, the content-based image retrieval (CBIR), which focuses on the exploration of image feature extraction methods, has been widely investigated. The term of feature extraction is used in two cases: application-based feature expression and mathematical approaches for dimensionality reduction. Feature expression is a technique of describing the image color, texture and shape information with feature descriptors; thus, obtaining effective image features expression is the key to extracting high-level semantic information. However, most of the previous studies regarding image feature extraction and expression methods in the CBIR have not performed systematic research. This paper aims to introduce the basic image low-level feature expression techniques for color, texture and shape features that have been developed in recent years.

Design/methodology/approach

First, this review outlines the development process and expounds the principle of various image feature extraction methods, such as color, texture and shape feature expression. Second, some of the most commonly used image low-level expression algorithms are implemented, and the benefits and drawbacks are summarized. Third, the effectiveness of the global and local features in image retrieval, including some classical models and their illustrations provided by part of our experiment, are analyzed. Fourth, the sparse representation and similarity measurement methods are introduced, and the retrieval performance of statistical methods is evaluated and compared.

Findings

The core of this survey is to review the state of the image low-level expression methods and study the pros and cons of each method, their applicable occasions and certain implementation measures. This review notes that image peculiarities of single-feature descriptions may lead to unsatisfactory image retrieval capabilities, which have significant singularity and considerable limitations and challenges in the CBIR.

Originality/value

A comprehensive review of the latest developments in image retrieval using low-level feature expression techniques is provided in this paper. This review not only introduces the major approaches for image low-level feature expression but also supplies a pertinent reference for those engaging in research regarding image feature extraction.

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Article

Padmapriya Nammalwar, Ovidiu Ghita and Paul F. Whelan

The purpose of this paper is to propose a generic framework based on the colour and the texture features for colour‐textured image segmentation. The framework can be…

Abstract

Purpose

The purpose of this paper is to propose a generic framework based on the colour and the texture features for colour‐textured image segmentation. The framework can be applied to any real‐world applications for appropriate interpretation.

Design/methodology/approach

The framework derives the contributions of colour and texture in image segmentation. Local binary pattern and an unsupervised k‐means clustering are used to cluster pixels in the chrominance plane. An unsupervised segmentation method is adopted. A quantitative estimation of colour and texture performance in segmentation is presented. The proposed method is tested using different mosaic and natural images and other image database used in computer vision. The framework is applied to three different applications namely, Irish script on screen images, skin cancer images and sediment profile imagery to demonstrate the robustness of the framework.

Findings

The inclusion of colour and texture as distributions of regions provided a good discrimination of the colour and the texture. The results indicate that the incorporation of colour information enhanced the texture analysis techniques and the methodology proved effective and efficient.

Originality/value

The novelty lies in the development of a generic framework using both colour and texture features for image segmentation and the different applications from various fields.

Details

Sensor Review, vol. 30 no. 1
Type: Research Article
ISSN: 0260-2288

Keywords

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Article

A. Vadivel, Shamik Sural and A.K. Majumdar

The main obstacle in realising semantic‐based image retrieval from the web is that it is difficult to capture semantic description of an image in low‐level features…

Abstract

Purpose

The main obstacle in realising semantic‐based image retrieval from the web is that it is difficult to capture semantic description of an image in low‐level features. Text‐based keywords can be generated from web documents to capture semantic information for narrowing down the search space. The combination of keywords and various low‐level features effectively increases the retrieval precision. The purpose of this paper is to propose a dynamic approach for integrating keywords and low‐level features to take advantage of their complementary strengths.

Design/methodology/approach

Image semantics are described using both low‐level features and keywords. The keywords are constructed from the text located in the vicinity of images embedded in HTML documents. Various low‐level features such as colour histograms, texture and composite colour‐texture features are extracted for supplementing keywords.

Findings

The retrieval performance is better than that of various recently proposed techniques. The experimental results show that the integrated approach has better retrieval performance than both the text‐based and the content‐based techniques.

Research limitations/implications

The features of images used for capturing the semantics may not always describe the content.

Practical implications

The indexing mechanism for dynamically growing features is challenging while practically implementing the system.

Originality/value

A survey of image retrieval systems for searching images available on the internet found that no internet search engine can handle both low‐level features and keywords as queries for retrieving images from WWW so this is the first of its kind.

Details

Online Information Review, vol. 33 no. 6
Type: Research Article
ISSN: 1468-4527

Keywords

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Article

Kun Wei, Yong Dai and Bingyin Ren

This paper aims to propose an identification method based on monocular vision for cylindrical parts in cluttered scene, which solves the issue that iterative closest point…

Abstract

Purpose

This paper aims to propose an identification method based on monocular vision for cylindrical parts in cluttered scene, which solves the issue that iterative closest point (ICP) algorithm fails to obtain global optimal solution, as the deviation from scene point cloud to target CAD model is huge in nature.

Design/methodology/approach

The images of the parts are captured at three locations by a camera amounted on a robotic end effector to reconstruct initial scene point cloud. Color signatures of histogram of orientations (C-SHOT) local feature descriptors are extracted from the model and scene point cloud. Random sample consensus (RANSAC) algorithm is used to perform the first initial matching of point sets. Then, the second initial matching is conducted by proposed remote closest point (RCP) algorithm to make the model get close to the scene point cloud. Levenberg Marquardt (LM)-ICP is used to complete fine registration to obtain accurate pose estimation.

Findings

The experimental results in bolt-cluttered scene demonstrate that the accuracy of pose estimation obtained by the proposed method is higher than that obtained by two other methods. The position error is less than 0.92 mm and the orientation error is less than 0.86°. The average recognition rate is 96.67 per cent and the identification time of the single bolt does not exceed 3.5 s.

Practical implications

The presented approach can be applied or integrated into automatic sorting production lines in the factories.

Originality/value

The proposed method improves the efficiency and accuracy of the identification and classification of cylindrical parts using a robotic arm.

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Article

Chih‐Fong Tsai and Wei‐Chao Lin

Content‐based image retrieval suffers from the semantic gap problem: that images are represented by low‐level visual features, which are difficult to directly match to…

Abstract

Purpose

Content‐based image retrieval suffers from the semantic gap problem: that images are represented by low‐level visual features, which are difficult to directly match to high‐level concepts in the user's mind during retrieval. To date, visual feature representation is still limited in its ability to represent semantic image content accurately. This paper seeks to address these issues.

Design/methodology/approach

In this paper the authors propose a novel meta‐feature feature representation method for scenery image retrieval. In particular some class‐specific distances (namely meta‐features) between low‐level image features are measured. For example the distance between an image and its class centre, and the distances between the image and its nearest and farthest images in the same class, etc.

Findings

Three experiments based on 190 concrete, 130 abstract, and 610 categories in the Corel dataset show that the meta‐features extracted from both global and local visual features significantly outperform the original visual features in terms of mean average precision.

Originality/value

Compared with traditional local and global low‐level features, the proposed meta‐features have higher discriminative power for distinguishing a large number of conceptual categories for scenery image retrieval. In addition the meta‐features can be directly applied to other image descriptors, such as bag‐of‐words and contextual features.

Content available
Article

T. Mahalingam and M. Subramoniam

Surveillance is the emerging concept in the current technology, as it plays a vital role in monitoring keen activities at the nooks and corner of the world. Among which…

Abstract

Surveillance is the emerging concept in the current technology, as it plays a vital role in monitoring keen activities at the nooks and corner of the world. Among which moving object identifying and tracking by means of computer vision techniques is the major part in surveillance. If we consider moving object detection in video analysis is the initial step among the various computer applications. The main drawbacks of the existing object tracking method is a time-consuming approach if the video contains a high volume of information. There arise certain issues in choosing the optimum tracking technique for this huge volume of data. Further, the situation becomes worse when the tracked object varies orientation over time and also it is difficult to predict multiple objects at the same time. In order to overcome these issues here, we have intended to propose an effective method for object detection and movement tracking. In this paper, we proposed robust video object detection and tracking technique. The proposed technique is divided into three phases namely detection phase, tracking phase and evaluation phase in which detection phase contains Foreground segmentation and Noise reduction. Mixture of Adaptive Gaussian (MoAG) model is proposed to achieve the efficient foreground segmentation. In addition to it the fuzzy morphological filter model is implemented for removing the noise present in the foreground segmented frames. Moving object tracking is achieved by the blob detection which comes under tracking phase. Finally, the evaluation phase has feature extraction and classification. Texture based and quality based features are extracted from the processed frames which is given for classification. For classification we are using J48 ie, decision tree based classifier. The performance of the proposed technique is analyzed with existing techniques k-NN and MLP in terms of precision, recall, f-measure and ROC.

Details

Applied Computing and Informatics, vol. 1 no. 1
Type: Research Article
ISSN: 2634-1964

Keywords

Content available

Abstract

Details

Assembly Automation, vol. 27 no. 4
Type: Research Article
ISSN: 0144-5154

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