局部线性嵌入的跨模态散列方法：LCLCH

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"基于标签一致的局部线性嵌入的跨模态散列" 这篇研究论文主要探讨了在跨模态检索应用中，如何利用一种新型的哈希方法——基于标签一致的局部线性嵌入的跨模态散列（Label Consistent Locally Linear Embedding based Cross-modal Hashing, LCLCH）来提升效率和效果。哈希方法在处理多模态数据时，由于其能够将高维数据压缩成低维二进制码，从而极大地降低了存储和检索的复杂度，因此在跨模态检索领域得到了广泛的关注。传统的哈希方法尽管有效，但存在一些局限性。它们往往未能充分捕捉到基于特征的相似性一致性，即不同模态数据间共享的结构信息，同时也未能充分利用标签的一致性，即同一实体在不同模态下的语义关联。此外，大多数哈希方法在量化过程中会损失大量信息，导致检索性能下降。 LCLCH方法针对上述问题进行了创新。它首先保留了非线性流形中的数据结构，通过局部线性嵌入（Locally Linear Embedding, LLE）技术，保持了数据点与其邻域内点的拓扑关系。接着，引入了标签一致性约束，以确保不同模态数据的哈希编码在语义上保持一致。这种方法不仅考虑了特征间的相似性，还强调了不同模态数据之间的语义相关性，从而提高了检索的准确性。在实际应用中，LCLCH通过最小化量化误差来优化哈希码的生成，减少了信息的丢失。同时，通过联合优化哈希编码和标签一致性，该方法能够学习到更具判别性的二进制码，进而提高跨模态检索的性能。关键词：跨模态检索、离散优化、哈希论文摘要指出，LCLCH方法在保持非线性数据结构的同时，有效地结合了标签的一致性，这使得它在跨模态检索任务中具有更高的性能和实用性。通过实验验证，LCLCH相比于其他现有方法，能显著改善检索精度和效率，尤其在处理大规模多模态数据集时，其优势更为明显。这篇研究论文提出了一个新颖的跨模态哈希策略，旨在解决传统方法在保持特征相似性和语义一致性方面的不足，以及量化损失的问题。LCLCH方法为跨模态检索提供了一个强大的工具，对于未来的研究和应用具有重要的参考价值。

data and cross-modal retrieval task. On the other hand, instead of using Laplacian Eigenmap to solve the problem of maintaining

manifold structure, we directly utilize Locally Linear Embedding to obtain better results in local neighbor information preserving.

2.2. Quantization in hashing

In hashing methods, obtaining binary codes requires a quantization process. In the generation of hash codes, Hypercube

Quantization (Gong, Lazebnik, Gordo, & Perronnin, 2013) is commonly used, which quantizes data points into a set of vertices of a

hypercube. When quantization center points are ﬁxed at 1 or −1, the quantization is the Hypercube Quantization, and the problem is

written as a mathematical expression.

x ymin

(1)

where

y { 1, 1}

. The typical methods are Iterative Quantization (Gong et al., 2013). Isotropic Hashing (Kong & Li, 2012), Har-

monious Hashing (Xu et al., 2013), Angular Quantization (Gong, Kumar, Verma, & Lazebnik, 2012). Taking Iterative Quantization as

an example, it reduces the dimensionality of the original data by principal component analysis, and then it maps the original data to

the vertices of a hypercube to solve the projection matrix with the smallest quantization error. Recently, some clustering methods and

classiﬁcation methods (Nie, Tian, & Li, 2018) have introduced such quantization method, and hashing methods for single modal

retrieval utilize it to achieve better performance. Currently, some quantization based methods have been proposed for cross-modal

retrieval. Shared Predictive Cross-Modal Deep Quantization (Yang et al., 2018) learns the quantizer in a common subspace by

semantic label alignment. Diﬀerently from it, we adopt the method of orthogonal rotation quantization on the common semantic

space to reduce time consumption. In addition, cross-modal or multi-modal retrieval methods rarely take into account quantization

algorithms, therefore, quantization is a worthwhile consideration in the ﬁeld of cross-modal hashing.

2.3. Cross-modal hashing

Cross-modal hashing methods have the advantages of satisﬁed retrieval eﬃciency and low storage cost in dealing with large-scale

data. They can also be divided into supervised ones and unsupervised ones based on whether label information is used during training

process. Unsupervised cross-modal hashing methods explore the intra- and inter-modal similarity to learn the hash codes. For in-

stance, Inter-Media Hashing (IMH) (Song et al., 2013) explores a linear regression model to learn hashing functions for each media

type and introduces inter-media consistency and intra-media consistency to ﬁnd a common Hamming space. Both Collective Matrix

Factorization Hashing (CMFH) (Ding et al., 2014) and Cluster-based Joint Matrix Factorization Hashing (J-CMFH) (Rafailidis &

Crestani, 2016) utilize matrix factorization model to capture the latent structure between data and learn uniﬁed hash codes in a

common latent space. In the uniﬁed latent space, J-CMFH learns cluster representations for cross-modal instances and captures the

inter-modality and intra-modality similarities. Unsupervised Semantic-Preserving Adversarial Hashing (USePAH) (Deng et al., 2019)

designs a generative adversarial framework and constructs feature similarity and neighbor similarity to guide the learning process,

and learns the hash code in an unsupervised manner. Latent Semantic Sparse Hashing (LSSH) (Zhou et al., 2014) learns the latent

factors by sparse coding of image modality and matrix factorization of text modality respectively. Although these methods can

explore correlation of heterogeneous data, the learned hash codes are not discriminative enough and semantic similarity is not well

preserved in Hamming space. Thus they cannot obtain further performance improvement to adapt real-world applications.

For supervised ones, they consider label information to train the model. General speaking, since they utilize semantic labels to

mitigate the semantic gap and make the learned hash codes more discriminative in semantics, supervised methods can achieve better

retrieval performance than unsupervised ones. A series of meaningful and representative supervised cross-modal hashing methods

have been proposed. For instance, Semantic Correlation Maximization (SCM) (Zhang & Li, 2014) maximizes the semantic correlation

to learn the hash codes by utilizing the semantic information. Based on CMFH (Ding et al., 2014), Supervised Matrix Factorization

hashing (SMFH) (Tang et al., 2016) maintains semantic similarity in Hamming space by constraining the hash function with semantic

label information. Semantic Preserving Hashing (SePH) (Lin et al., 2015) constructs the aﬃnity matrix by supervised information and

learns hash codes by utilizing K-L divergence to approximate the aﬃnity matrix. Pairwise Relationship Guided Deep Hashing

(PRGDH) (Yang, Deng et al., 2017) uses diﬀerent pairwise constraints for inter-modal and intra-modal data and generates dis-

criminative hash codes by end-to-end deep network. It also uses the semantic similarity matrix in the hash code learning process.

Generalized Semantic Preserving Hashing (GSePH) (Mandal, Chaudhury, & Biswas, 2017) constructs similarity matrix in single-label

paired, single-label unpaired, multi-label paired and multi-label unpaired scenarios and learns hash codes by minimizing the simi-

larity matrix and hamming distance. Discrete Cross-modal Hashing (DCH) (Xu et al., 2017) takes semantic label as a classiﬁcation and

learns the hash codes by a discrete bit-wise optimization manner. These supervised methods are very representative work and have

achieved good results. But it is worth noting that most methods consider semantic label as pairwise similarities and neglect the

category information.

3. Our method

In this section, we describe the proposed method in detail, and show the whole framewok in Fig. 1.

H. Zeng, et al.

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