本文選題：圖像檢索 + 圖像自動標(biāo)注�。� 參考：《內(nèi)蒙古大學(xué)》2017年碩士論文

【摘要】：在文字搜索日益成熟的今天,圖像搜索的需求也越來越大。但由于圖像包含了更大的信息量,并且在存儲方式上很難體現(xiàn)出圖像的語義特征,所以圖像的索引和檢索顯得十分困難。利用成熟的文字搜索方式,將圖像搜索轉(zhuǎn)換為文字搜索不失為是一個好的方法。而這種方式依賴于對圖像語義的準(zhǔn)確描述。這種描述方式隨著圖像數(shù)量的快速增長,手工實現(xiàn)無論從時間上還是費用上都太過昂貴,已經(jīng)不能滿足人們的需要。為了更好地實現(xiàn)圖像檢索,實現(xiàn)自動的生成標(biāo)注圖像信息的方法已經(jīng)非常必要和緊迫。同時,圖像自動標(biāo)注算法在許多領(lǐng)域都有重要的應(yīng)用。如針對視頻的檢索,針對盲人的電影劇情描述等等。本文使用神經(jīng)網(wǎng)絡(luò)的方法,對圖像自動標(biāo)注的方法進(jìn)行了研究,具體內(nèi)容如下:1、圖像自動標(biāo)注深深依賴于算法對圖片對象識別的準(zhǔn)確度。只有識別出圖像中存在的對象,才能進(jìn)一步的生成標(biāo)注信息。而一張圖像中的對象可能會有很多,每個對象有不同的屬性,甚至有些對象會包含子對象,有些對象之間甚至可能會出現(xiàn)覆蓋現(xiàn)象。本文方法首先要在眾多的對象中進(jìn)行篩選,選出重要的對象,才能進(jìn)行識別。2、圖像自動標(biāo)注包含了對象識別模型和自然語言生成模型兩個計算機視覺系統(tǒng)。對象識別模型使用了卷積神經(jīng)網(wǎng)絡(luò)模型進(jìn)行處理。而自然語言模型則使用循環(huán)神經(jīng)網(wǎng)絡(luò)模型進(jìn)行處理。兩者共同組建成一個完整的系統(tǒng)。
[Abstract]:In the text search increasingly mature today, image search demand is also growing. However, because the image contains more information and it is difficult to reflect the semantic features of the image in the storage mode, it is very difficult to index and retrieve the image. It is a good method to transform image search into text search by using mature text search method. This approach depends on the accurate description of image semantics. With the rapid increase of the number of images, the manual implementation is too expensive in time and cost to meet the needs of people. In order to achieve better image retrieval, it is necessary and urgent to automatically generate and annotate image information. At the same time, automatic image tagging algorithm has important applications in many fields. Such as video retrieval, for blind film plot description and so on. In this paper, the neural network method is used to study the automatic image tagging method. The specific contents are as follows: 1. The automatic image tagging depends heavily on the accuracy of the algorithm for the recognition of image objects. Only when the objects in the image are identified can the tagging information be further generated. There may be many objects in an image, each object has different properties, even some objects may contain child objects, and some objects may even have overlay phenomenon. In this paper, first of all, we have to screen many objects and select the important objects before we can recognize .2. the automatic image tagging includes two computer vision systems: object recognition model and natural language generation model. The object recognition model is processed by convolution neural network model. The natural language model is processed by the circulatory neural network model. Together, they form a complete system.
【學(xué)位授予單位】：內(nèi)蒙古大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41;TP183

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本文編號：1795756