本文選題：商品圖像分類 + 稀疏編碼�。� 參考：《北京郵電大學(xué)》2016年碩士論文

【摘要】：互聯(lián)網(wǎng)的迅速普及和基礎(chǔ)設(shè)施的不斷完善帶來了電子商務(wù)的飛速發(fā)展。隨著虛擬網(wǎng)絡(luò)中的商品種類和數(shù)量急劇增加,如何向客戶有效展示這些豐富的商品信息成為電子商務(wù)在智能化過程中遇到的重要問題。商品圖像作為商品在互聯(lián)網(wǎng)這個虛擬空間中最主要的信息載體,對其進(jìn)行基于內(nèi)容的自動分類能夠為交易雙方迅速檢索商品信息、合理制定商品放置策略以及對客戶進(jìn)行個性化推薦提供幫助,從而提高電子商務(wù)市場的整體效能。本文在商品圖像分類現(xiàn)有研究的基礎(chǔ)上,借鑒通用圖像分類算法,結(jié)合商品圖像物體擺放位置居中、拍攝背景簡單、細(xì)節(jié)信息豐富等特點,提出改進(jìn)的基于內(nèi)容的商品圖像分類算法,具體工作如下:(1)針對商品圖像的特點,將圖像自適應(yīng)地劃分為具有大量商品信息的前景區(qū)域和缺少有用信息的空白背景區(qū)域。在前景區(qū)域中將具有明顯局部特征的區(qū)域劃分為特征區(qū)域,提取稀疏SIFT特征;將灰度變化較為平緩的區(qū)域劃分為輔助特征區(qū)域,提取稠密SIFT特征;在空白的背景區(qū)域不提取特征點。采用SCSPM得到各區(qū)域的描述向量并根據(jù)融合函數(shù)進(jìn)行連接并作為圖像最終的描述特征。將這些特征輸入支持向量機進(jìn)行分類,實驗結(jié)果顯示使用自適應(yīng)的融合SCSPM特征能比傳統(tǒng)方法在商品圖像分類上獲得更好的結(jié)果。(2)針對傳統(tǒng)SPM方法雖然記錄了圖像的空間位置信息,但不能體現(xiàn)視覺單詞在特定位置所具有的分類能力的問題,本文提出一種基于熵的SPM空間加權(quán)方法。不同的視覺單詞在不同類別出現(xiàn)的概率可能不同,根據(jù)信息理論可以利用熵的概念來描述不同單詞的這種分類能力,因而在計算單詞權(quán)重時融入視覺單詞在該區(qū)域的分類信息能夠進(jìn)一步提高視覺單詞的區(qū)分能力。(3)針對部分商品圖像集使用單個SVM分類器分類準(zhǔn)確率難以得到進(jìn)一步提高的現(xiàn)象。本文提出一種以弱SVM作為AdaBoost算法分量分類器的多分類器聯(lián)合決策方法AdaBoostSVM。依次訓(xùn)練一組分量分類器,在每一輪中給每個訓(xùn)練樣本賦予一個權(quán)重,表明每個分量分類器對其關(guān)注程度,通過調(diào)整權(quán)重將分類器聚焦到更容易錯分的那些樣本點以獲得更好的分類性能。本文利用MATLAB對商品圖片分類過程進(jìn)行模擬,實驗結(jié)果表明該方法能夠?qū)ι唐穲D像進(jìn)行有效分類,在實驗圖像集上平均分類準(zhǔn)確率達(dá)到87%。
[Abstract]:The rapid popularization of the Internet and the continuous improvement of infrastructure brought about the rapid development of electronic commerce. With the rapid increase in the types and quantities of goods in virtual networks, how to effectively display these abundant commodity information to customers has become an important problem in the process of intelligent e-commerce. As the main information carrier of commodity in the virtual space of Internet, commodity image can be automatically classified based on content, which can quickly retrieve commodity information for both sides of the transaction. To improve the overall efficiency of e-commerce market, we can make a reasonable product placement strategy and provide help for customer personalized recommendation. On the basis of the existing research of commodity image classification, this paper draws lessons from the general image classification algorithm, combines the characteristics of the commodity image object placement in the middle, the shooting background is simple, the detail information is rich, etc. An improved content-based classification algorithm for commodity images is proposed. The main work is as follows: 1) according to the characteristics of commodity images, the image can be adaptively divided into foreground regions with a large amount of commodity information and blank background areas without useful information. In the foreground region, the region with obvious local features is divided into feature regions, the sparse sift features are extracted, and the regions with gentle grayscale changes are divided into auxiliary feature areas to extract dense sift features. Feature points are not extracted in the blank background area. The description vectors of each region are obtained by SCSPM and connected according to the fusion function and used as the final description feature of the image. Input these features into support vector machines for classification, Experimental results show that using adaptive fusion SCSPM features can obtain better results than traditional methods in commodity image classification. However, it can not reflect the classification ability of visual words in a particular position. In this paper, an entropy based SPM spatial weighting method is proposed. Different visual words may have different probability of appearing in different categories. According to the information theory, the concept of entropy can be used to describe the classification ability of different words. Therefore, it is difficult to improve the classification accuracy of single SVM classifier for some commodity image sets by incorporating the classification information of visual words in this region when calculating the weight of words. In this paper, a multi-classifier joint decision method, AdaBoost SVM, using weak SVM as the component classifier of AdaBoost algorithm is proposed. In turn, a group of component classifiers are trained, and each training sample is given a weight in each round, indicating that each component classifier pays attention to it. By adjusting the weights, the classifier is focused on the sample points that are more easily misclassified to obtain better classification performance. In this paper, we use MATLAB to simulate the process of commodity image classification. The experimental results show that the method can effectively classify the commodity image, and the average classification accuracy on the experimental image set is 87%.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TP391.41

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