Retracted: Content-Based E-Commerce Image Classification Research

Zhang, Xiaoli

doi:10.1109/access.2020.3018877

Cited by 12 publications

(3 citation statements)

References 11 publications

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“…Study [18] uses both Hu moments and dominant color descriptor on the pixels. Research [19] uses LBP-DBN training algorithm with adaptive momentum learning rate to overcome the issues of longer training time and reduction in classification accuracy. The author in [20] uses Directional Magnitude Local Hexadecimal Patterns, for image retrieval, by reducing the semantic gap problem using a learning-based approach.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Content Based Image Retrieval using Deep Feature Extraction and Similarity Matching

Mathews¹,

Sejal²,

Venugopal³

2022

IJACSA

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Image retrieval using a textual query becomes a major challenge mainly due to human perception subjectivity and the impreciseness of image annotations. These drawbacks can be overcome by focusing on the content of images rather than on the textual descriptions of images. Traditional feature extraction techniques demand for expert knowledge to select the limited feature types and are also sensitive to changing imaging conditions. Deep feature extraction using Convolutional Neural Network (CNN) are a solution to these drawbacks as they can learn the feature representations automatically. This work carries out a detailed performance comparison of various pretrained models of CNN in feature extraction. Features are extracted from men footwear and women clothing datasets using the VGG16, VGG19, InceptionV3, Xception and ResNet50 models. Further, these extracted features are used for classification using SVM, Random Forest and K-Nearest Neighbors classifiers. Results of feature extraction and image retrieval show that VGG19, Inception and Xception features perform well with feature extraction, achieving a good image classification accuracy of 97.5%. These results are further justified by performing a comparison of image retrieval efficiency, with the extracted features and similarity metrics. This work also compares the accuracy obtained by features extracted by the selected pre-trained CNN models with the results obtained using conventional classification techniques on CIFAR 10 dataset. The features extracted using CNN can be used in image-based systems like recommender systems, where images have to be analyzed to generate item profiles.

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Section: Related Workmentioning

confidence: 99%

Analysis of Content Based Image Retrieval using Deep Feature Extraction and Similarity Matching

Mathews¹,

Sejal²,

Venugopal³

2022

IJACSA

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“…Such important advantages of ensemble solutions, as resistance to distortions of individual components and ensuring higher accuracy of data analysis or training, are discussed. Some works are of particular scientific interest with deep applied content [21]- [23], but they do not contain universal statistical application, in particular, in terms of the use of existing statistical distributions.…”

Section: Related Workmentioning

confidence: 99%

Methods of Classification of Images on the Basis of the Values of Statistical Distributions for the Composition of Structural Description Components

et al. 2021

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The article considers the problem of image recognition in computer vision systems. The results of the development of the method for image classification, using a structural approach, are presented. The classification method is based on calculating the values of statistical distributions for the set of description descriptors. The distribution vector for a fixed set of classes is based on the calculation of the degree of similarity with the integral characteristics for the descriptions of the etalon base. Two options for constructing the classifier on the principles of objectetalon and object descriptoretalon, which differ in the degree of integration of the solution, are proposed. The median for the set of vectors describing the etalon is used as the aggregate characteristic of the etalon descriptions. The experimental evaluation of the effectiveness of the developed classifiers in terms of verification of performance and evaluation of the probability of correct classification according to the results of processing of applied images based on three etalons are carried out. The values of precision and completeness indicators for the method object descriptoretalon, which has demonstrated the significant advantage over the integrated approach, are given. At the same time, both proposed in the experiment methods classify the set of etalons without error. The methods of mathematical statistics, intellectual data analysis, image recognition, the apparatus for calculating the relevance of the system of the features, as well as simulation modelling, are used in this research. Based on the study and the experiment, it was found that the processing time of the images for the developed method is approximately 7 times less than for the traditional method, without reducing the accuracy. The perspective of further research is to study the interference immunity of the developed methods and evaluate their applied effectiveness for three-dimensional image collections.

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“…Several CBIR [20][21][22] methods utilize the deep learning approaches using VGGNet, generative adversarial networks (GAN), deep convolutional GAN, infoGAN are used to fetch similar images and also tested on the various big data platforms [23,24]. Feature extraction using the local binary pattern (LBP) and deep belief networks were proposed for CBIR on multiple datasets [25]. The rest of the paper consists of the 3 sections.…”

Section: Figure 1 the Proposed Outcome For Our Visual Recommendationsmentioning

confidence: 99%

e-SimNet: a visual similar product recommender system for E-commerce

Addagarla¹,

Amalanathan²

2021

IJEECS

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<span>Visual similarity recommendations have an immense role in E-commerce portals. Fetching the appropriate similar products and suggesting to the buyers based on the product image's visual features is complex. Here in our research, we presented an efficient E-commerce similar product network model (e-SimNet) for visually similar recommendations. To achieve our objective, we have performed image feature extraction and generating embeddings using deep learning techniques and built an Index tree using the approximate nearest neighbor oh yeah (ANNOY) algorithm. Further, we have fetches top-N the near similar items using distance measure. We have benchmarked our model in terms of accuracy, error rate, and results show that better than other state-of-the-art approaches with 96.22% of accuracy.</span>

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Retracted: Content-Based E-Commerce Image Classification Research

Cited by 12 publications

References 11 publications

Analysis of Content Based Image Retrieval using Deep Feature Extraction and Similarity Matching

Analysis of Content Based Image Retrieval using Deep Feature Extraction and Similarity Matching

Methods of Classification of Images on the Basis of the Values of Statistical Distributions for the Composition of Structural Description Components

e-SimNet: a visual similar product recommender system for E-commerce

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