基于多特征融合的商品圖像分類
發(fā)布時間：2018-10-15 17:37
【摘要】：由于電子商務網(wǎng)站的成功發(fā)展及網(wǎng)絡多媒體技術的迅速普及,在線購物已經(jīng)成為一種方便、快捷、廉價且時尚的購物方式,但隨之而來的是圖像數(shù)據(jù)呈幾何級數(shù)的增長,對如此超大規(guī)模的多媒體數(shù)據(jù)進行有效管理,并提供迅速、準確的檢索服務是一個極具挑戰(zhàn)性的課題。目前,電子購物網(wǎng)站的搜索服務仍然依賴基于文本的搜索引擎,標注并關聯(lián)商品的基本信息,對于用戶難以準確地描述的樣式、花紋、造型等特有屬性缺少進一步的標注,將基于內(nèi)容的圖像自動分類引入電子商務,緩解商品圖像數(shù)據(jù)庫的管理壓力和提高消費者對商品的檢索效率,是當前電子商務領域的迫切需求。 本文以在線購物商品的圖像為基礎,構建了一個手工標注商品特殊屬性的數(shù)據(jù)集,并以大量實驗關注不同的圖像特征對商品圖像屬性的分類檢測結果。主要的研究內(nèi)容和貢獻如下： 首先,本文針對原始且粗略的在線商品圖像集,從購物用戶最關注的色彩和款式兩個重要屬性出發(fā),基于商品圖像特性進行了顏色、紋理和形狀分布的剖析,確定運用HSV顏色空間對商品圖像提取顏色矩和顏色直方圖特征,并采用局部二值模式、梯度局部二值模式、二元梯度輪廓和方向梯度直方圖描述紋理信息和形狀信息聯(lián)合表達商品圖像的款式屬性,通過實驗證明了這些特征具有的分類性能。 其次,文中詳細介紹了不同底層特征對于商品顏色和款式屬性的分類方法細節(jié),對兩個屬性層面的不同特征進行特征級的融合,構建復合的特征向量并通過實驗檢驗特征組合分類的性能變化,實驗結果表明,商品圖像的分類準確率得到了部分提升。 最后,雖然每種特征具備特有的分類性能,但不同特征與分類器決策的相關性沒有得到綜合利用,采用不同內(nèi)核的分類算法針對特定特征會有突出的表現(xiàn),因此本文引入了多內(nèi)核學習方法改進分類決策,設計和運用大量實驗測試了顏色、紋理、形狀特征聯(lián)合描述商品圖像屬性的能力,對比了多組實驗的結果并分析了特征在多核學習中的分類性能。
[Abstract]:With the successful development of e-commerce websites and the rapid popularization of multimedia technology, online shopping has become a convenient, fast, cheap and fashionable way of shopping. It is a challenging task to manage multimedia data on such a large scale effectively and to provide fast and accurate retrieval services. At present, the search service of electronic shopping website still relies on the search engine based on text, marking and associating the basic information of the goods, and lack of further annotation for the unique attributes such as style, pattern, modeling and so on, which are difficult to describe accurately by the user. It is an urgent need to introduce the automatic classification of content-based images into electronic commerce to relieve the management pressure of commodity image database and to improve the retrieval efficiency of consumers in the field of electronic commerce. Based on the images of online shopping items, this paper constructs a data set of manually tagging the special attributes of commodities, and pays close attention to the classification and detection results of commodity image attributes by a large number of experiments. The main research contents and contributions are as follows: first of all, aiming at the original and rough online commodity image set, this paper starts from the two important attributes of color and style that the shopper pays most attention to, and carries on the color based on the commodity image characteristic. Based on the analysis of texture and shape distribution, HSV color space is used to extract color moments and color histogram features from commodity images, and local binary mode and gradient local binary mode are adopted. Binary gradient contour and directional gradient histogram are used to describe texture information and shape information to express the style attributes of commercial images. The classification performance of these features is proved by experiments. Secondly, this paper introduces the classification methods of different bottom features for commodity color and style attributes in detail, and combines the different features of the two attribute levels at the feature level. The experimental results show that the classification accuracy of commodity images has been partially improved. Finally, although each feature has its own classification performance, the correlation between different features and classifier decision is not comprehensively utilized. Therefore, this paper introduces a multi-kernel learning method to improve the classification decision, designs and uses a large number of experiments to test the ability of color, texture and shape features to describe the attributes of commodity images. The results of multi-group experiments are compared and the classification performance of features in multi-core learning is analyzed.
【學位授予單位】：西南交通大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：TP391.41
本文編號：2273298
[Abstract]:With the successful development of e-commerce websites and the rapid popularization of multimedia technology, online shopping has become a convenient, fast, cheap and fashionable way of shopping. It is a challenging task to manage multimedia data on such a large scale effectively and to provide fast and accurate retrieval services. At present, the search service of electronic shopping website still relies on the search engine based on text, marking and associating the basic information of the goods, and lack of further annotation for the unique attributes such as style, pattern, modeling and so on, which are difficult to describe accurately by the user. It is an urgent need to introduce the automatic classification of content-based images into electronic commerce to relieve the management pressure of commodity image database and to improve the retrieval efficiency of consumers in the field of electronic commerce. Based on the images of online shopping items, this paper constructs a data set of manually tagging the special attributes of commodities, and pays close attention to the classification and detection results of commodity image attributes by a large number of experiments. The main research contents and contributions are as follows: first of all, aiming at the original and rough online commodity image set, this paper starts from the two important attributes of color and style that the shopper pays most attention to, and carries on the color based on the commodity image characteristic. Based on the analysis of texture and shape distribution, HSV color space is used to extract color moments and color histogram features from commodity images, and local binary mode and gradient local binary mode are adopted. Binary gradient contour and directional gradient histogram are used to describe texture information and shape information to express the style attributes of commercial images. The classification performance of these features is proved by experiments. Secondly, this paper introduces the classification methods of different bottom features for commodity color and style attributes in detail, and combines the different features of the two attribute levels at the feature level. The experimental results show that the classification accuracy of commodity images has been partially improved. Finally, although each feature has its own classification performance, the correlation between different features and classifier decision is not comprehensively utilized. Therefore, this paper introduces a multi-kernel learning method to improve the classification decision, designs and uses a large number of experiments to test the ability of color, texture and shape features to describe the attributes of commodity images. The results of multi-group experiments are compared and the classification performance of features in multi-core learning is analyzed.
【學位授予單位】：西南交通大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：TP391.41
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