基于離散圖哈希的圖像檢索算法研究
發(fā)布時(shí)間：2018-08-29 16:52
【摘要】：隨著互聯(lián)網(wǎng)的高速發(fā)展,圖像數(shù)據(jù)規(guī)模呈爆發(fā)式增長(zhǎng),研究如何在海量圖像數(shù)據(jù)中高效檢索出用戶感興趣的圖像具有重大意義。傳統(tǒng)的基于內(nèi)容的圖像檢索算法主要采用圖像的內(nèi)容特征進(jìn)行相似度匹配,存在特征之間“語義鴻溝”、特征維度高、存儲(chǔ)空間大、檢索效率低等問題,用基于哈希的圖像檢索算法可以有效彌補(bǔ)上述不足。目前基于哈希的圖像檢索算法檢索準(zhǔn)確率和檢索效率還無法令人滿意,本課題為解決這兩個(gè)問題,提出基于離散圖哈希的圖像檢索算法,主要研究成果如下:針對(duì)圖像檢索中的特征提取問題,提出一種基于拉普拉斯圖模型的多視圖非負(fù)特征融合算法。采用非負(fù)矩陣分解技術(shù),將每一種視圖特征進(jìn)行特征轉(zhuǎn)換,分解后的非負(fù)特征表達(dá)能力更強(qiáng)。每種視圖特征僅僅是圖像語義信息的一個(gè)表現(xiàn)層面,采用拉普拉斯圖模型將多種視圖的非負(fù)特征嵌入到一個(gè)統(tǒng)一的潛在空間,在該空間中融合后的特征可以更完整地表達(dá)圖像語義特征。在構(gòu)造拉普拉斯圖模型時(shí),引入“錨點(diǎn)”技術(shù),降低拉普拉斯矩陣的計(jì)算復(fù)雜度。根據(jù)算法模型,在公開數(shù)據(jù)集上進(jìn)行了實(shí)驗(yàn),驗(yàn)證本文研究的多特征融合算法要比單特征檢索準(zhǔn)確率高。為了構(gòu)建高效索引,提出了一種有監(jiān)督機(jī)器學(xué)習(xí)的離散圖哈希圖像檢索算法。通過學(xué)習(xí)哈希函數(shù),將原始空間中的數(shù)據(jù)特征映射到漢明空間,保持?jǐn)?shù)據(jù)相似性,在漢明空間中計(jì)算哈希碼相似度。在學(xué)習(xí)哈希函數(shù)時(shí),利用數(shù)據(jù)的標(biāo)簽信息對(duì)圖像語義信息的表示作用,采用有監(jiān)督的機(jī)器學(xué)習(xí)方法,使用離散圖優(yōu)化框架,直接對(duì)約束變量為離散值的目標(biāo)函數(shù)優(yōu)化,避免了傳統(tǒng)方法采用“松弛”策略導(dǎo)致哈希編碼質(zhì)量較低,提高了檢索精度,采用離散循環(huán)坐標(biāo)下降法,逐位生成所有訓(xùn)練樣本的哈希碼,提高了哈希碼的生成速度,使用該算法在公開數(shù)據(jù)集上與其他主流哈希方法進(jìn)行對(duì)比實(shí)驗(yàn),驗(yàn)證了本文提出的哈希算法在圖像檢索時(shí)的高效性。為了驗(yàn)證多視圖非負(fù)特征融合后的特征有效性,用離散圖哈希算法對(duì)該特征進(jìn)行檢索實(shí)驗(yàn),實(shí)驗(yàn)表明該特征與離散圖哈希算法結(jié)合,在圖像檢索中能夠提高檢索準(zhǔn)確性。
[Abstract]:With the rapid development of the Internet, the scale of image data is increasing explosively. It is of great significance to study how to efficiently retrieve the images of interest to users in the massive image data. The traditional content-based image retrieval algorithm mainly uses the content feature of the image to carry on the similarity matching, which has the problem of "semantic gap" between the features, the feature dimension is high, the storage space is large, the retrieval efficiency is low, and so on. Hash-based image retrieval algorithm can effectively compensate for the above deficiencies. At present, the accuracy and efficiency of hash based image retrieval algorithm are not satisfactory. In order to solve these two problems, this paper proposes an image retrieval algorithm based on discrete image hashing. The main research results are as follows: aiming at the feature extraction problem in image retrieval, a multi-view non-negative feature fusion algorithm based on Laplace diagram model is proposed. The non-negative matrix decomposition technique is used to transform the features of each view, and the non-negative feature expression is stronger after the decomposition. Each view feature is only a representation of the semantic information of the image. The Laplace diagram model is used to embed the non-negative features of multiple views into a unified potential space. The fused features in this space can express image semantic features more completely. In order to reduce the computational complexity of Laplace matrix, the "anchor point" technique is introduced in the construction of Laplace diagram model. According to the algorithm model, experiments are carried out on the open data set to verify that the multi-feature fusion algorithm studied in this paper is more accurate than the single-feature retrieval algorithm. In order to construct an efficient index, a supervised machine learning discrete image retrieval algorithm is proposed. By learning the hash function, the data feature in the original space is mapped to the hamming space, and the similarity of the data is maintained, and the similarity of the hash code is calculated in the hamming space. When learning hash function, we use label information of data to represent semantic information of image, adopt supervised machine learning method, use discrete graph optimization framework, and directly optimize objective function with constraint variable as discrete value. The traditional method of "relaxation" strategy is used to avoid the lower hash coding quality and improve the retrieval accuracy. The discrete cyclic coordinate descent method is used to generate the hash codes of all training samples bit by bit, and the generation speed of the hash codes is improved. The proposed algorithm is compared with other popular hash methods on the open data set, and the efficiency of the proposed hash algorithm in image retrieval is verified. In order to verify the feature validity of multi-view non-negative feature fusion, a discrete Tuhash algorithm is used to retrieve the feature. The experiment results show that the feature can improve the retrieval accuracy in image retrieval by combining the feature with the discrete image hash algorithm.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41
本文編號(hào)：2211817
[Abstract]:With the rapid development of the Internet, the scale of image data is increasing explosively. It is of great significance to study how to efficiently retrieve the images of interest to users in the massive image data. The traditional content-based image retrieval algorithm mainly uses the content feature of the image to carry on the similarity matching, which has the problem of "semantic gap" between the features, the feature dimension is high, the storage space is large, the retrieval efficiency is low, and so on. Hash-based image retrieval algorithm can effectively compensate for the above deficiencies. At present, the accuracy and efficiency of hash based image retrieval algorithm are not satisfactory. In order to solve these two problems, this paper proposes an image retrieval algorithm based on discrete image hashing. The main research results are as follows: aiming at the feature extraction problem in image retrieval, a multi-view non-negative feature fusion algorithm based on Laplace diagram model is proposed. The non-negative matrix decomposition technique is used to transform the features of each view, and the non-negative feature expression is stronger after the decomposition. Each view feature is only a representation of the semantic information of the image. The Laplace diagram model is used to embed the non-negative features of multiple views into a unified potential space. The fused features in this space can express image semantic features more completely. In order to reduce the computational complexity of Laplace matrix, the "anchor point" technique is introduced in the construction of Laplace diagram model. According to the algorithm model, experiments are carried out on the open data set to verify that the multi-feature fusion algorithm studied in this paper is more accurate than the single-feature retrieval algorithm. In order to construct an efficient index, a supervised machine learning discrete image retrieval algorithm is proposed. By learning the hash function, the data feature in the original space is mapped to the hamming space, and the similarity of the data is maintained, and the similarity of the hash code is calculated in the hamming space. When learning hash function, we use label information of data to represent semantic information of image, adopt supervised machine learning method, use discrete graph optimization framework, and directly optimize objective function with constraint variable as discrete value. The traditional method of "relaxation" strategy is used to avoid the lower hash coding quality and improve the retrieval accuracy. The discrete cyclic coordinate descent method is used to generate the hash codes of all training samples bit by bit, and the generation speed of the hash codes is improved. The proposed algorithm is compared with other popular hash methods on the open data set, and the efficiency of the proposed hash algorithm in image retrieval is verified. In order to verify the feature validity of multi-view non-negative feature fusion, a discrete Tuhash algorithm is used to retrieve the feature. The experiment results show that the feature can improve the retrieval accuracy in image retrieval by combining the feature with the discrete image hash algorithm.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41
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相關(guān)期刊論文 前1條
1 歐新宇;伍嘉;朱恒;李佶;;基于深度自學(xué)習(xí)的圖像哈希檢索方法[J];計(jì)算機(jī)工程與科學(xué);2015年12期
相關(guān)博士學(xué)位論文 前1條
1 金仲明;基于哈希算法的海量多媒體數(shù)據(jù)檢索研究[D];浙江大學(xué);2015年
，本文編號(hào)：2211817
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