Improving Product Classification Using Images

Kannan, Anitha; Talukdar, Partha; Rasiwasia, Nikhil; Ke, Qifa

doi:10.1109/icdm.2011.79

Cited by 29 publications

(20 citation statements)

References 22 publications

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“…Recently, deep learning technology such as CNN was used to extract image features. It was also utilized for image classification and pattern recognition [16][17][18]. Research reported that the performance of a CNN model can be improved when pre-trained and pre-weighted values are used with a large volume of image data such as that of ImageNet [19,20].…”

Section: Image2vec-based Feature Extraction Modelmentioning

confidence: 99%

Development of Fashion Product Retrieval and Recommendations Model Based on Deep Learning

Lee

et al. 2020

Electronics

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The digitization of the fashion industry diversified consumer segments, and consumers now have broader choices with shorter production cycles; digital technology in the fashion industry is attracting the attention of consumers. Therefore, a system that efficiently supports the searching and recommendation of a product is becoming increasingly important. However, the text-based search method has limitations because of the nature of the fashion industry, in which design is a very important factor. Therefore, we developed an intelligent fashion technique based on deep learning for efficient fashion product searches and recommendations consisting of a Sketch-Product fashion retrieval model and vector-based user preference fashion recommendation model. It was found that the “Precision at 5” of the image-based similar product retrieval model was 0.774 and that of the sketch-based similar product retrieval model was 0.445. The vector-based preference fashion recommendation model also showed positive performance. This system is expected to enhance consumers’ satisfaction by supporting users in more effectively searching for fashion products or by recommending fashion products before they begin a search.

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Section: Image2vec-based Feature Extraction Modelmentioning

confidence: 99%

Development of Fashion Product Retrieval and Recommendations Model Based on Deep Learning

Lee

et al. 2020

Electronics

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“…Although there are a lot of approaches for products matching and classification based on text features, only a few are using image features for the given task. Kannan et al [10] proposed one of the first approaches for product categorization that besides text features uses image features. The approach is based on Confusion Driven Probabilistic Fusion++, which is cognizant of the disparity in the discriminative power of different types of signals and hence makes use of the confusion matrix of dominant signal (text) to prudently leverage the weaker signal (image), for an improved performance.…”

Section: Product Classificationmentioning

confidence: 99%

“…Dataset The latest extraction of WebDataCommons includes over 5 billion entities marked up by one of the three main HTML markup languages (i.e., Microdata, Microformats and RDFa) and has been retrieved from the CommonCrawl 2014 corpus 10 . From this dataset we focus on product entities annotated with Microdata using the schema.org vocabulary.…”

Section: Unstructured Product Offers -Wdc Microdatamentioning

confidence: 99%

A machine learning approach for product matching and categorization

Ristoski

Petrovski

Mika

et al. 2018

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Consumers today have the option to purchase products from thousands of e-shops. However, the completeness of the product specifications and the taxonomies used for organizing the products differ across different e-shops. To improve the consumer experience, approaches for product integration on the Web are needed. In this paper, we present an approach that leverages deep learning techniques in combination with standard classification approaches for product matching and categorization. In our approach we use structured product data as supervision for training feature extraction models able to extract attribute-value pairs from textual product descriptions. To minimize the need for lots of data for supervision, we use neural language models to produce word embeddings from large quantities of publicly available product data marked up with Microdata, which boost the performance of the feature extraction model, thus leading to better product matching and categorization performances. Furthermore, we use a deep Convolutional Neural Network to produce image embeddings from product images, which further improve the results on both tasks.

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“…While some approaches do use computer vision to classify products, they face many challenges such as high training time, low accuracy, requirement of powerful computing resources, etc. [6].…”

Section: Importance and Aim Of The Projectmentioning

confidence: 99%

“…While some products on ecommerce websites are categorized manually, this number is very small [6]. Having millions of products on the website, it is not possible to manually categorize each and every one of them, which is why an automatic classification system becomes of utmost importance for any ecommerce website.…”

Section: Introductionmentioning

confidence: 99%

Optimizing E-Commerce Product Classification Using Transfer Learning

Khanuja

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The global e-commerce market is snowballing at a rate of 23% per year. In 2017, retail e-commerce users were 1.66 billion and sales worldwide amounted to 2.3 trillion US dollars, and e-retail revenues are projected to grow to 4.88 trillion USD in 2021. With the immense popularity that e-commerce has gained over past few years comes the responsibility to deliver relevant results to provide rich user experience. In order to do this, it is essential that the products on the ecommerce website be organized correctly into their respective categories. Misclassification of products leads to irrelevant results for users which not just reflects badly on the website, it could also lead to lost customers. With ecommerce sites nowadays providing their portal as a platform for third party merchants to sell their products as well, maintaining a consistency in product categorization becomes difficult. Therefore, automating this process could be of great utilization. This task of automation done on the basis of text could lead to discrepancies since the website itself, its various merchants, and users, all could use different terminologies for a product and its category. Thus, using images becomes a plausible solution for this problem. Dealing with images can best be done using deep learning in the form of convolutional neural networks. This is a computationally expensive task, and in order to keep the accuracy of a traditional convolutional neural network while reducing the hours it takes for the model to train, this project aims at using a technique called transfer learning. Transfer learning refers to sharing the knowledge gained from one task for another where new model does not need to be trained from scratch in order to reduce the time it takes for training. This project aims at using various product images belonging to five categories from an ecommerce platform and developing an algorithm that can accurately classify products in their respective categories while taking as less time as possible. The goal is to first test the performance of transfer learning against traditional convolutional networks. Then the next step is to apply transfer learning to the downloaded dataset and assess its performance on the accuracy and time taken to classify test data that the model has never seen before.

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Improving Product Classification Using Images

Cited by 29 publications

References 22 publications

Development of Fashion Product Retrieval and Recommendations Model Based on Deep Learning

Development of Fashion Product Retrieval and Recommendations Model Based on Deep Learning

A machine learning approach for product matching and categorization

Optimizing E-Commerce Product Classification Using Transfer Learning

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