【摘要】：互聯(lián)網(wǎng)中色情圖片傳播泛濫,對其自動識別與過濾越來越重要。在本課題中,主要針對網(wǎng)絡上常見的單人色情寫真類圖片,提出了基于感興趣區(qū)域(Regions of Interest,ROIs)檢測的不良圖片識別算法。傳統(tǒng)的不良圖像檢測算法主要將人體皮膚部分作為感興趣區(qū)域,從皮膚檢測的結果中提取與膚色相關的一些信息,如膚色像素所占面積比例等,再結合皮膚的顏色、紋理、形狀等特征進行分類。這種方法雖然簡單直觀,但是在保證較高正檢率的前提下,往往誤檢率也往往較高,尤其對于類如泳裝模特等裸露較多的正常圖片,效果不甚理想。我們在總結了已有方法不足的基礎上,提出了將人體軀干部位作為感興趣區(qū)域的不良圖片檢測方法。首先使用基于Poselet姿態(tài)部件的人體軀干檢測方法定位出與色情信息密切相關的軀干區(qū)域,結合此興趣區(qū)域和SIFT特征訓練高斯混合模型,獲取具有判別力的Fisher向量,再利用SVM學習算法訓練得到裸露胸部的分類器。然而,由于人體外觀變化很大,軀干檢測器輸出的置信度最大的位置往往較軀干真實的位置有一定的偏移。為了克服這一缺點,我們進一步提出了一種自適應的算法,即根據(jù)軀干檢測器輸出的置信度自適應的選擇多個軀干候選區(qū)域,并通過集成多個區(qū)域的判別結果來得到最終結果。此外,為了訓練基于軀干的SVM分類器和驗證算法的有效性,本文通過互聯(lián)網(wǎng)下載的方式收集了一個包含30,000幅單人色情寫真圖片的大規(guī)模數(shù)據(jù)集,并對色情部位進行了標注,標注信息可用于自動生成訓練數(shù)據(jù)。本文提出的基于軀干的自適應分類算法在收集的大規(guī)模數(shù)據(jù)集上達到了91.7%的識別精度,明顯高于傳統(tǒng)膚色模型的識別結果。文中采用的基于姿態(tài)部件的感興趣區(qū)域檢測能夠獲取與色情信息更相關的信息,因而相比較于傳統(tǒng)方法,在較為準確地檢測不良圖片的同時,有效地降低皮膚裸露較多的正常圖像的誤檢率,達到了實際應用的要求。
[Abstract]:The spread of pornographic images in the Internet is becoming more and more important for its automatic recognition and filtering. In this paper, aiming at the common single person pornographic pictures on the network, a bad image recognition algorithm based on (Regions of Interestrois detection is proposed. The traditional bad image detection algorithm mainly takes the human skin as the region of interest, extracts some information related to skin color from the results of skin detection, such as the proportion of skin color pixels to the area, and then combines the skin color, texture, etc. Shape and other features are classified. Although this method is simple and intuitive, under the premise of ensuring higher positive detection rate, the false detection rate is often higher, especially for the more exposed normal pictures such as swimsuit models, the effect is not very good. On the basis of summarizing the shortcomings of the existing methods, we propose a method for detecting the bad images of the region of interest by taking the trunk part of the human body as the region of interest. Firstly, the human torso detection method based on Poselet pose component is used to locate the torso region which is closely related to pornographic information. Combined with this region of interest and SIFT features, the mixed Gao Si model is trained to obtain the discriminant Fisher vector. Then the SVM learning algorithm is used to train the bare chest classifier. However, due to the great changes in human appearance, the position of maximum confidence in the output of the trunk detector is often offset to the true position of the trunk. In order to overcome this shortcoming, we further propose an adaptive algorithm, which adaptively selects multiple trunk candidate regions according to the confidence output of the trunk detector, and obtains the final results by integrating the discriminant results of multiple regions. In addition, in order to train the trunk based SVM classifier and verify the validity of the algorithm, this paper collects a large scale data set including 30000 portraits of a single person by the way of Internet download, and marks the pornographic parts. Annotated information can be used to automatically generate training data. The self-adaptive classification algorithm based on trunk in this paper achieves 91.7% recognition accuracy on the collected large-scale data set, which is obviously higher than the recognition result of traditional skin color model. In this paper, the region of interest detection based on attitude components can obtain more relevant information than traditional methods, so it is more accurate to detect bad images at the same time. It can effectively reduce the false detection rate of normal images with more skin exposure and meet the requirements of practical application.
【學位授予單位】：南京航空航天大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP391.41
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本文編號：2157803