本文選題：購物圖像搜索　切入點(diǎn)：商品提取　出處：《西南交通大學(xué)》2012年碩士論文　論文類型：學(xué)位論文

【摘要】：由于電子商務(wù)網(wǎng)站的成功發(fā)展,在線購物已經(jīng)成為一種方便、快捷、廉價(jià)的購物方式,隨之而來的是圖像數(shù)據(jù)呈現(xiàn)幾何級(jí)數(shù)增長,如何對(duì)如此超大規(guī)模的購物圖像進(jìn)行有效搜索成為近年來學(xué)術(shù)界和工業(yè)界的研究熱點(diǎn)。目前Google和阿里巴巴等已經(jīng)提供了查找視覺相似商品的搜索服務(wù),但是這些搜索引擎由于對(duì)復(fù)雜背景的圖像直接提取形狀、紋理、顏色等的全局視覺特征而受到圖像背景噪聲的干擾,因此無法取得理想的搜索效果。為了提高購物圖像的搜索準(zhǔn)確度,必須去除圖像的復(fù)雜背景,即提取出圖像中的商品目標(biāo)。本文針對(duì)包和衣服類復(fù)雜背景購物圖像的商品提取問題,提出了與傳統(tǒng)機(jī)器學(xué)習(xí)不同的檢查方法,旨在用以提高購物圖像的搜索準(zhǔn)確度。本文的主要內(nèi)容和貢獻(xiàn)如下： 第一,提出了購物圖像主目標(biāo)提取算法,該算法主要針對(duì)不含有模特的圖像提取商品目標(biāo)。購物圖像雖然背景復(fù)雜卻有這樣的特點(diǎn),商品目標(biāo)一般被置于接近圖像中心的位置,并且目標(biāo)對(duì)象應(yīng)該占圖像足夠比例以醒目。于是通過利用基于圖的快速分割算法對(duì)圖像對(duì)象的識(shí)別能力,以及主目標(biāo)的空間位置分布特性和區(qū)域大小特性,本文提出了與傳統(tǒng)機(jī)器學(xué)習(xí)不同的檢測(cè)方法算法來獲取目標(biāo)對(duì)象。 第二,提出了購物圖像多目標(biāo)提取算法,該算法主要處理含有模特的購物圖像。購物圖像中的模特一方面為商品提取增加了難度,另一方面也為找到衣物提供了線索。該算法首先利用人臉和膚色等的先驗(yàn)知識(shí)大致定位可能的衣物區(qū)域；接著根據(jù)高斯混合模型分析了圖像的背景和衣物模型,并加入空間信息修正這些模型；最后根據(jù)模型準(zhǔn)確地得到衣物。 第三,實(shí)現(xiàn)了購物搜索系統(tǒng)。利用主目標(biāo)、多目標(biāo)提取算法去除購物圖像的背景干擾后再提取圖像的顏色、SIFT (Scale Invariant Feature Transform)特征,前臺(tái)采用Grub Cut分割算法與用戶交互,最后利用歐式距離和BoW (Bag of Words)分別匹配顏色和SIFT特征。通過實(shí)驗(yàn)一方面證明了兩種提取算法的有效性,另一方面說明本文的搜索系統(tǒng)能提高購物圖像的檢索準(zhǔn)確度。
[Abstract]:Due to the successful development of e-commerce websites, online shopping has become a convenient, fast and cheap way of shopping, followed by the geometric growth of image data. How to effectively search such large scale shopping images has become a hot research topic in academia and industry in recent years. At present, Google and Alibaba have provided search services to find visual similar products. However, these search engines are disturbed by background noise because of extracting the global visual features of shape, texture, color and so on directly from the image of complex background. In order to improve the search accuracy of the shopping image, the complex background of the image must be removed. In this paper, a different checking method from traditional machine learning is proposed to extract commodities from shopping images with complicated background of bags and clothes. This paper aims to improve the search accuracy of shopping images. The main contents and contributions of this paper are as follows:. First, the main object extraction algorithm of shopping image is proposed. The algorithm is mainly aimed at extracting commodity target without models. Although the background of shopping image is complex, it has such characteristics. Commodity objects are generally placed close to the center of the image, and the target object should account for a sufficient proportion of the image to stand out. Thus, the ability to recognize the image object is achieved by using a fast segmentation algorithm based on a graph. As well as the spatial distribution characteristics and region size characteristics of the main target, this paper proposes a different detection algorithm from the traditional machine learning algorithm to obtain the target object. Secondly, a multi-object extraction algorithm for shopping images is proposed, which mainly deals with shopping images with models. On the one hand, models in shopping images make it more difficult to extract goods. On the other hand, it also provides clues to find clothing. Firstly, the algorithm uses the prior knowledge of face and skin color to roughly locate the possible clothing region. Then, according to Gao Si mixed model, the background and clothing model of the image are analyzed. These models are modified by adding spatial information. Finally, the clothing is accurately obtained according to the model. Thirdly, a shopping search system is implemented, which uses the main target, multi-target extraction algorithm to remove the background interference of the shopping image, and then extracts the color sift scale Invariant Feature transform feature of the image. The foreground uses Grub Cut segmentation algorithm to interact with the user. Finally, the Euclidean distance and BoW bag of Wordsare used to match the color and SIFT features respectively. On the one hand, the validity of the two extraction algorithms is proved by experiments, on the other hand, the search system in this paper can improve the retrieval accuracy of shopping images.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：TP391.41

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本文編號(hào)：1631371