【摘要】：基于內(nèi)容的圖像分析技術(shù)在圖像檢索中的應用已經(jīng)引起越來越廣泛的關(guān)注，圖像搜索重排序技術(shù)是其中一種利用圖像的視覺信息對初始文本搜索結(jié)果進行再次分析與排序的新技術(shù)。有效的視覺表征是其中的關(guān)鍵技術(shù)之一，然而由于視覺特征具有高維及存在“語義鴻溝”等問題，直接應用現(xiàn)有視覺特征難以獲得較好的排序性能。維數(shù)約簡方法可以在一定程度上克服這些缺點，但是傳統(tǒng)的維數(shù)約簡維數(shù)約簡算法往往是針對分類任務提出的，，并不適合于排序問題。排序?qū)W習與分類任務并不等同，因此設計適用于圖像搜索重排序?qū)W習的維數(shù)約簡算法顯得尤為重要。為此，本文有針對性地進行了若干研究，主要工作及創(chuàng)新為： （1）基于PCA（Principal Component Analysis）降維后每個維度具有的不同比重的貢獻率，提出了一種基于主成分分析的相似度計算方法SM-PCA，并在此基礎(chǔ)上提出了一種利用少量標注樣本即可得到較好的排序性能的直推式半監(jiān)督重排序方法。在該方法中采用迭代的方式計算擴展訓練樣本集合，并利用訓練樣本集合訓練排序模型，最后對待排序的樣本進行重排序，在網(wǎng)絡搜索引擎下載的圖像數(shù)據(jù)庫驗證了算法性能的有效性。 （2）提出了一種基于典型相關(guān)性分析的排序維數(shù)約簡算法。在排序?qū)W習中廣泛存在的是樣本的相關(guān)性等級信息，其與樣本的類別標簽信息有很大的不同。基于此，在典型相關(guān)性分析算法的基礎(chǔ)上，把排序問題中樣本的相關(guān)性等級信息引入到維數(shù)約簡技術(shù)中，設計適用于多模態(tài)數(shù)據(jù)的維數(shù)約簡算法。將其應用到圖像搜索重排序中，大量實驗表明所提算法可以顯著地改善圖像檢索性能。
[Abstract]:The application of content-based image analysis technology in image retrieval has attracted more and more attention. Image search reordering is one of the new techniques to reanalyze and sort the original text search results using the visual information of the image. Effective visual representation is one of the key technologies. However, due to the high dimension of visual features and the existence of "semantic gap", it is difficult to obtain better ranking performance by direct application of existing visual features. Dimension reduction can overcome these shortcomings to some extent, but the traditional dimension reduction algorithm is often proposed for classification tasks and is not suitable for sorting problems. Sorting learning is not the same as classification task, so it is very important to design dimension reduction algorithm which is suitable for image search and resort learning. Therefore, this paper has carried out a number of targeted studies. The main work and innovations are as follows: (1) based on the contribution rate of different proportion of each dimension after dimension reduction by PCA (Principal Component Analysis), In this paper, a similarity calculation method based on principal component analysis (SM-PCA,) is proposed, and a direct-push semi-supervised reordering method based on a small number of labeled samples is proposed. In this method, an iterative method is used to calculate the set of extended training samples, and the training set is used to train the sorting model. Finally, the sorted samples are reordered. The performance of the algorithm is validated in the image database downloaded by the network search engine. (2) A sort dimension reduction algorithm based on canonical correlation analysis is proposed. The correlation level information of samples exists widely in sorting learning, which is very different from the category label information of samples. Based on this, based on the typical correlation analysis algorithm, the correlation level information of the sample in the sorting problem is introduced into the dimension reduction technology, and a dimension reduction algorithm suitable for multi-modal data is designed. A large number of experiments show that the proposed algorithm can significantly improve the performance of image retrieval.
【學位授予單位】：天津大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：TP391.41

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相關(guān)期刊論文 前1條

1 王黎;帥建梅;;圖像重排序中與查詢相關(guān)的圖像相似性度量[J];計算機系統(tǒng)應用;2010年11期

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