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| Title: | 以廣告影像感知特徵建立智慧型分類
Established intelligent classification based on the feature of advertising image |
| Authors: | 古雅琪
Ku, Ya-Chi |
| Contributors: | 羅崇銘
Lo, Chung-Ming
古雅琪
Ku, Ya-Chi |
| Keywords: | 電子商務
商品形象
色彩構成
卷積神經網路
影像分類
E-commerce
Product image
Color extraction
Convolutional neural network
Image classification |
| Date: | 2022 |
| Issue Date: | 2022-09-02 14:58:58 (UTC+8) |
| Abstract: | 受到時代演進、資訊技術提升以及環境改變的影響，電子商務的蓬勃發展 已成為趨勢。電子商務販售的商品眾多，因此其最大的考驗莫過於商品歸類， 而透過後設資料的描述能使商品歸類更有效率。目前電子商務商品的後設資料 多透過手動輸入及分類，需耗費大量的人力資源，若能夠以自動化分類商品便 能夠減輕成本及人力資源的負擔。本研究為建立高適用性的自動化商品分類系 統，以聯合國標準商品與服務編碼分類系統作為依據，將 momo 購物網作為研 究目標，擷取 9 大類商品影像資料集後，根據影像所傳達出商品形象的色彩構 成與卷積神經網路兩種方法建立自動化分類系統，以解決在商品多樣化的電子 商務下，人工分類的負擔與可能的失誤。結果顯示，雖然企業會根據消費者認 知設計商品的色彩，但利用色彩構成分類 9 類商品圖的最高準確率為 46.50%; 利用卷積神經網路可以達到最高準確率為 83.11%，兩者相較之下利用卷積神經 網路能夠更好地建立自動化商品分類，可以做為電子商務平台上自動化商品分 類的系統技術，讓商品歸類更有效率。
Over recent years, e-commerce become more and more popular with the development of information technology and the environment. E-commerce offers numerous products, and its biggest challenge is how to classify product categories precisely and efficiently with metadata. Currently e-commerce lunches and classify metadata manually, which consumes lots of labor cost, classifying products automatically can decrease both cost and mistakes of manual classification. In this paper, we narrowed the dataset down to 9 categories, by using UNSPSC classification systems as standard to classify products from “momo”, hoping that we can automatically classify categories based on the color extraction and convolutional neural network from product image. Although enterprises will design the color of products according to customer’s perceptions, our results indicate that the accuracy rate of convolutional neural network is 83.11% higher than color extraction 46.50%, which means using convolutional neural network can establish intelligent classification better. |
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| Description: | 碩士
國立政治大學
圖書資訊與檔案學研究所
109155006 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0109155006 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU202201196 |
| Appears in Collections: | [圖書資訊與檔案學研究所] 學位論文
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