Utilizing Context Information to Enhance Content-Based Image Classification

2011 ◽  
Vol 2 (3) ◽  
pp. 34-51 ◽  
Author(s):  
Qiusha Zhu ◽  
Lin Lin ◽  
Mei-Ling Shyu ◽  
Dianting Liu
Keyword(s):  
Image Classification ◽  
Multiple Correspondence Analysis ◽  
Context Information ◽  
Web Based ◽  
Web Environment ◽  
Classification Framework ◽  
Text Information ◽  
Human Effort ◽  
Content Information ◽  
Traditional Image

Traditional image classification relies on text information such as tags, which requires a lot of human effort to annotate them. Therefore, recent work focuses more on training the classifiers directly on visual features extracted from image content. The performance of content-based classification is improving steadily, but it is still far below users’ expectation. Moreover, in a web environment, HTML surrounding texts associated with images naturally serve as context information and are complementary to content information. This paper proposes a novel two-stage image classification framework that aims to improve the performance of content-based image classification by utilizing context information of web-based images. A new TF*IDF weighting scheme is proposed to extract discriminant textual features from HTML surrounding texts. Both content-based and context-based classifiers are built by applying multiple correspondence analysis (MCA). Experiments on web-based images from Microsoft Research Asia (MSRA-MM) dataset show that the proposed framework achieves promising results.

Utilizing Context Information to Enhance Content-Based Image Classification

2013 ◽  
pp. 114-130
Author(s):  
Qiusha Zhu ◽  
Lin Lin ◽  
Mei-Ling Shyu ◽  
Dianting Liu
Keyword(s):  
Image Classification ◽  
Multiple Correspondence Analysis ◽  
Context Information ◽  
Web Based ◽  
Web Environment ◽  
Classification Framework ◽  
Text Information ◽  
Human Effort ◽  
Textual Features ◽  
Content Information

Traditional image classification relies on text information such as tags, which requires a lot of human effort to annotate them. Therefore, recent work focuses more on training the classifiers directly on visual features extracted from image content. The performance of content-based classification is improving steadily, but it is still far below users’ expectation. Moreover, in a web environment, HTML surrounding texts associated with images naturally serve as context information and are complementary to content information. This paper proposes a novel two-stage image classification framework that aims to improve the performance of content-based image classification by utilizing context information of web-based images. A new TF*IDF weighting scheme is proposed to extract discriminant textual features from HTML surrounding texts. Both content-based and context-based classifiers are built by applying multiple correspondence analysis (MCA). Experiments on web-based images from Microsoft Research Asia (MSRA-MM) dataset show that the proposed framework achieves promising results.

An automated mobile app labeling framework based on primary motivations for smartphone use

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Anteneh Ayanso ◽  
Mingshan Han ◽  
Morteza Zihayat
Keyword(s):  
Classification Scheme ◽  
Mobile Apps ◽  
Mobile App ◽  
Machine Learning Algorithms ◽  
User Needs ◽  
Context Information ◽  
Web Based ◽  
Content Type ◽  
Classification Framework ◽  
Textual Data

Purpose This paper aims to propose an automated mobile app labeling framework based on a novel app classification scheme that is aligned with users’ primary motivations for using smartphones. The study addresses the gaps in incorporating the needs of users and other context information in app classification as well as recommendation systems. Design/methodology/approach Based on a corpus of mobile app descriptions collected from Google Play store, this study applies extensive text analytics and topic modeling procedures to profile mobile apps within the categories of the classification scheme. Sufficient number of representative and labeled app descriptions are then used to train a classifier using machine learning algorithms, such as rule-based, decision tree and artificial neural network. Findings Experimental results of the classifiers show high accuracy in automatically labeling new apps based on their descriptions. The accuracy of the classification results suggests a feasible direction in facilitating app searching and retrieval in different Web-based usage environments. Research limitations/implications As a common challenge in textual data projects, the problem of data size and data quality issues exists throughout the multiple phases of experiments. Future research will extend the data collection scope in many aspects to address the issues that constrained the current experiments. Practical implications These empirical experiments demonstrate the feasibility of textual data analysis in profiling apps and user context information. This study also benefits app developers by improving app descriptions through a better understanding of user needs and context information. Finally, the classification framework can also guide practitioners in customizing products and services beyond mobile apps where context information and user needs play an important role. Social implications Given the widespread usage and applications of smartphones today, the proposed app classification framework will have broader implications to different Web-based application environments. Originality/value While there have been other classification approaches in the literature, to the best of the authors’ knowledge, this framework is the first study on building an automated app labeling framework based on primary motivations of smartphone usage.

Automated Skin Cancer Detection Web-Based Application and Review of Some Image Classification Algorithm

2020 ◽  
Author(s):  
Siddhesh Bhojane ◽  
Krishna Shrestha ◽  
Sanghmitra Bharadwaj ◽  
Ritul Yadav ◽  
Fenil Ribinwala ◽  
...  
Keyword(s):  
Skin Cancer ◽  
Image Classification ◽  
Cancer Detection ◽  
Classification Algorithm ◽  
Web Based ◽  
Skin Cancer Detection

scholarly journals A Commodity Classification Framework Based on Machine Learning for Analysis of Trade Declaration

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 964
Author(s):  
Mingshu He ◽  
Xiaojuan Wang ◽  
Chundong Zou ◽  
Bingying Dai ◽  
Lei Jin
Keyword(s):  
Machine Learning ◽  
Chinese Text ◽  
Decision Fusion ◽  
High Rate ◽  
Text Recognition ◽  
Fusion Model ◽  
Commodity Trade ◽  
Classification Framework ◽  
Text Information ◽  
Application Fields

Text, voice, images and videos can express some intentions and facts in daily life. By understanding these contents, people can identify and analyze some behaviors. This paper focuses on the commodity trade declaration process and identifies the commodity categories based on text information on customs declarations. Although the technology of text recognition is mature in many application fields, there are few studies on the classification and recognition of customs declaration goods. In this paper, we proposed a classification framework based on machine learning (ML) models for commodity trade declaration that reaches a high rate of accuracy. This paper also proposed a symmetrical decision fusion method for this task based on convolutional neural network (CNN) and transformer. The experimental results show that the fusion model can make up for the shortcomings of the two original models and some improvements have been made. In the two datasets used in this paper, the accuracy can reach 88% and 99%, respectively. To promote the development of study of customs declaration business and Chinese text recognition, we also exposed the proprietary datasets used in this study.

scholarly journals A Hyperspectral Image Classification Framework with Spatial Pixel Pair Features

Sensors ◽  
2017 ◽  
Vol 17 (10) ◽  
pp. 2421 ◽  
Author(s):  
Lingyan Ran ◽  
Yanning Zhang ◽  
Wei Wei ◽  
Qilin Zhang
Keyword(s):  
Image Classification ◽  
Hyperspectral Image ◽  
Hyperspectral Image Classification ◽  
Classification Framework ◽  
Pixel Pair

A Multi-Stage Framework for Classification of Unconstrained Image Data From Mobile Phones

2018 ◽  
pp. 2387-2401
Author(s):  
Shashank Mujumdar ◽  
Dror Porat ◽  
Nithya Rajamani ◽  
L.V. Subramaniam
Keyword(s):  
Image Classification ◽  
Mobile Phones ◽  
Real World ◽  
Image Data ◽  
Complex Environments ◽  
Second Stage ◽  
Classification Framework ◽  
Multi Stage ◽  
Efficient Detection

During the past decade, the number of mobile electronic devices equipped with cameras has increased dramatically and so has the number of real-world applications for image classification. In many of these applications, the image data is captured in an uncontrolled manner and in complex environments and conditions under which existing image classification techniques may not perform well. In this paper, the authors provide a detailed description of an efficient multi-stage image classification framework that is robust enough to remain effective also under challenging imaging conditions, and demonstrate its effectiveness in the context of classification of real-world images of dumpsters captured by mobile phones in the metropolitan city of Hyderabad. Their system is able to achieve accurate classification of the cleanliness state of the dumpsters by utilizing a multi-stage approach, where the first stage is the efficient detection of the dumpster and the second stage is the classification of its state. The authors provide a detailed analysis of the performance of the system as well as comprehensive experimental results on real-world image data.

scholarly journals A Robust Rule-Based Ensemble Framework Using Mean-Shift Segmentation for Hyperspectral Image Classification

2019 ◽  
Vol 11 (17) ◽  
pp. 2057 ◽  
Author(s):  
Majid Shadman Roodposhti ◽  
Arko Lucieer ◽  
Asim Anees ◽  
Brett Bryan
Keyword(s):  
Image Classification ◽  
Hyperspectral Image ◽  
Mean Shift ◽  
Training Sample ◽  
Uncertainty Assessment ◽  
Hyperspectral Image Classification ◽  
Rule Based ◽  
Classification Framework ◽  
Rule Sets ◽  
Ensemble Algorithms

This paper assesses the performance of DoTRules—a dictionary of trusted rules—as a supervised rule-based ensemble framework based on the mean-shift segmentation for hyperspectral image classification. The proposed ensemble framework consists of multiple rule sets with rules constructed based on different class frequencies and sequences of occurrences. Shannon entropy was derived for assessing the uncertainty of every rule and the subsequent filtering of unreliable rules. DoTRules is not only a transparent approach for image classification but also a tool to map rule uncertainty, where rule uncertainty assessment can be applied as an estimate of classification accuracy prior to image classification. In this research, the proposed image classification framework is implemented using three world reference hyperspectral image datasets. We found that the overall accuracy of classification using the proposed ensemble framework was superior to state-of-the-art ensemble algorithms, as well as two non-ensemble algorithms, at multiple training sample sizes. We believe DoTRules can be applied more generally to the classification of discrete data such as hyperspectral satellite imagery products.

A Comparative Analysis of Reliability Assessment Methods for Web-Based Software

2013 ◽  
Vol 1 (4) ◽  
pp. 31-44 ◽  
Author(s):  
Jinhee Park ◽  
Yeong-Seok Seo ◽  
Jongmoon Baik
Keyword(s):  
Service Oriented Architecture ◽  
Reliability Assessment ◽  
Dynamic Environment ◽  
Assessment Methods ◽  
Software Systems ◽  
Web Based ◽  
Software Applications ◽  
Web Environment ◽  
Service Oriented ◽  
The Web

As web technology has advanced, many business software applications are built on the web. In such web environment, it has become very important to ensure the reliabilities of web-based software systems such as Software as a Service (SaaS) or Service-Oriented Architecture (SOA) based systems because service failures in those systems may have an effect on extensive users. With the comparison to the reliability studies on traditional software, there are only a few studies on the reliability of web-based software. The dynamic environment of the web makes it much more complicated to assess the reliabilities of web-based software. In this paper, the authors investigate the characteristics of reliability assessment methods for web-based software such as SaaS and SOA based software systems. The authors also evaluate those methods based on hypothetical execution scenarios to analyze the strengths and weaknesses of each method. This analysis helps us to identify remaining problems on the reliability research in the web environment and provides insight into possible solutions.

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