共查询到20条相似文献，搜索用时 15 毫秒
1.
《Information processing & management》2022,59(3):102919
Similarity search with hashing has become one of the fundamental research topics in computer vision and multimedia. The current researches on semantic-preserving hashing mainly focus on exploring the semantic similarities between pointwise or pairwise samples in the visual space to generate discriminative hash codes. However, such learning schemes fail to explore the intrinsic latent features embedded in the high-dimensional feature space and they are difficult to capture the underlying topological structure of data, yielding low-quality hash codes for image retrieval. In this paper, we propose an ordinal-preserving latent graph hashing (OLGH) method, which derives the objective hash codes from the latent space and preserves the high-order locally topological structure of data into the learned hash codes. Specifically, we conceive a triplet constrained topology-preserving loss to uncover the ordinal-inferred local features in binary representation learning. By virtue of this, the learning system can implicitly capture the high-order similarities among samples during the feature learning process. Moreover, the well-designed latent subspace learning is built to acquire the noise-free latent features based on the sparse constrained supervised learning. As such, the latent under-explored characteristics of data are fully employed in subspace construction. Furthermore, the latent ordinal graph hashing is formulated by jointly exploiting latent space construction and ordinal graph learning. An efficient optimization algorithm is developed to solve the resulting problem to achieve the optimal solution. Extensive experiments conducted on diverse datasets show the effectiveness and superiority of the proposed method when compared to some advanced learning to hash algorithms for fast image retrieval. The source codes of this paper are available at https://github.com/DarrenZZhang/OLGH . 相似文献
2.
3.
《Information processing & management》2020,57(6):102288
Deep hashing has been an important research topic for using deep learning to boost performance of hash learning. Most existing deep supervised hashing methods mainly focus on how to effectively preserve the similarity in hash coding solely depending on pairwise supervision. However, such pairwise similarity-preserving strategy cannot fully explore the semantic information in most cases, which results in information loss. To address this problem, this paper proposes a discriminative dual-stream deep hashing (DDDH) method, which integrates the pairwise similarity loss and the classification loss into a unified framework to take full advantage of label information. Specifically, the pairwise similarity loss aims to preserve the similarity and structural information of high-dimensional original data. Meanwhile, the designed classification loss can enlarge the margin between different classes which improves the discrimination of learned binary codes. Moreover, an effective optimization algorithm is employed to train the hash code learning framework in an end-to-end manner. The results of extensive experiments on three image datasets demonstrate that our method is superior to several state-of-the-art deep and non-deep hashing methods. Ablation studies and analysis further show the effectiveness of introducing the classification loss in the overall hash learning framework. 相似文献
4.
《Information processing & management》2023,60(3):103301
Hashing has been an emerging topic and has recently attracted widespread attention in multi-modal similarity search applications. However, most existing approaches rely on relaxation schemes to generate binary codes, leading to large quantization errors. In addition, amounts of existing approaches embed labels into the pairwise similarity matrix, leading to expensive time and space costs and losing category information. To address these issues, we propose an Efficient Discrete Matrix factorization Hashing (EDMH). Specifically, EDMH first learns the latent subspaces for individual modality through matrix factorization strategy, which preserves the semantic structure representation information of each modality. In particular, we develop a semantic label offset embedding learning strategy, improving the stability of label embedding regression. Furthermore, we design an efficient discrete optimization scheme to generate compact binary codes discretely. Eventually, we present two efficient learning strategies EDMH-L and EDMH-S to pursue high-quality hash functions. Extensive experiments on various widely-used databases verify that the proposed algorithms produce significant performance and outperform some state-of-the-art approaches, with an average improvement of 2.50% (for Wiki), 2.66% (for MIRFlickr) and 2.25% (for NUS-WIDE) over the best available results, respectively. 相似文献
5.
Yu Zhang Ye Yuan Fangda Guo Yishu Wang Guoren Wang 《Journal of The Franklin Institute》2018,355(4):1967-1990
The multi-modal retrieval is considered as performing information retrieval among different modalities of multimedia information. Nowadays, it becomes increasingly important in the information science field. However, it is so difficult to bridge the meanings of different multimedia modalities that the performance of multimodal retrieval is deteriorated now. In this paper, we propose a new mechanism to build the relationship between visual and textual modalities and to verify the multimodal retrieval. Specifically, this mechanism depends on the multimodal binary classifiers based on the Extreme Learning Machine (ELM) to verify whether the answers are related to the query examples. Firstly, we propose the multimodal probabilistic semantic model to rank the answers according to their generative probabilities. Furthermore, we build the multimodal binary classifiers to filter out unrelated answers. The multimodal binary classifiers are called the word classifiers. It can improve the performance of the multimodal probabilistic semantic model. The experimental results show that the multimodal probabilistic semantic model and the word classifiers are effective and efficient. Also they demonstrate that the word classifiers based on ELM not only can improve the performance of the probabilistic semantic model but also can be easily applied to other probabilistic semantic models. 相似文献
6.
《Information processing & management》2020,57(2):102165
With the rapid growth of multimedia data, cross-media hashing has gained more and more attention. However, most existing cross-modal hashing methods ignore the multi-label correlation and only apply binary similarity to measure the correlation between two instances. Most existing methods perform poorly in capturing the relevance between retrieval results and queries since binary similarity measurement has limited abilities to discriminate minor differences among different instances. In order to overcome the mentioned shortcoming, we introduce a novel notion of instance similarity method, which is used to evaluate the semantic correlation between two specific instances in training data. Base on the instance similarity, we also propose a novel deep instance hashing network, which utilizes instance similarity and binary similarity simultaneously for multi-label cross-model retrieval. The experiment results on two real datasets show the superiority of our proposed method, compared with a series of state-of-the-art cross-modal hashing methods in terms of several metric evaluations. 相似文献
7.
基于机器学习的图像检索机制的研究 总被引：1，自引：1，他引：1
主要针对当前基于低水平特征的图像检索机制不能捕获图像语义的状况 ,讨论了使用长期学习的方法来学习用户的相关反馈 ,以此推断语义 ,构造语义空间 ,并结合短期学习方法 ,通过运用学习监督机制来推断用户信息需求 ,优化查询 ,逐步提高搜索引擎的性能 相似文献
8.
国外标签本体研究进展 总被引：1，自引：0，他引：1
为解决folksonomies的问题,提出给标签、标注行为增加语义的标签本体,并利用语义网本体建模标注行为和folksonomies。标签本体的发展从关注标注活动发展到关注folksonomy(协同标注活动),并从标签含义的角度,创建MOAT跨越标注行为与语义检索的鸿沟。标签本体正走向统一、共享的新阶段。 相似文献
9.
本文借鉴文本检索领域的研究成果,利用标注文字信息描述单个模型的语义,采用语义树表达三维模型间的语义,基于WordNet计算检索关键词与语义树中节点的语义相似性,返回语义相关性强的模型。提出较灵活的返回策略,筛选各语义相关节点的代表模型,便于用户进一步优化检索结果。实验结果表明,基于语义树的三维模型检索方法能够提高信息检索的效率,具有较高的理论及应用价值。 相似文献
10.
《Information processing & management》2023,60(5):103416
Recently, graph neural network (GNN) has been widely used in sequential recommendation because of its powerful ability to capture high-order collaborative relations, greatly promoting recommendation performance. However, some existing GNN-based methods fail to make full use of multiple relevant features of nodes and ignore the impact of semantic association between nodes on extracting user preferences. To this end, we propose a multi-feature fused collaborative attention network MASR, which sufficiently learns the temporal and positional features of nodes, and innovatively measures the importance of these two features for analyzing the nodes’ dynamic patterns. In addition, we incorporate semantic-enriched contrastive learning into collaborative filtering to enhance the semantic association between nodes and reduce the noise from the structural neighborhood, which has a positive effect on the sequential recommendation. Compared with the baseline models, the performance of MASR on MovieLens, CDs and Beauty datasets is improved by 2.0%, 2.1% and 1.7% respectively, proving its effectiveness in the sequential recommendation. 相似文献
11.
《Information processing & management》2016,52(4):571-591
This work presents a content based semantics and image retrieval system for semantically categorized hierarchical image databases. Each module is designed with an aim to develop a system that works closer to human perception. Images are mapped to a multidimensional feature space, where images belonging a semantic are clustered and indexed to acquire its efficient representation. This helps in handling the existing variability or heterogeneity within this semantic. Adaptive combinations of the obtained depictions are utilized by the branch selection and pruning algorithms to identify some closer semantics and select only a part of the large hierarchical search space for actual search. So obtained search space is finally used to retrieve desired semantics and similar images corresponding to them. The system is evaluated in terms of accuracy of the retrieved semantics and precision-recall curves. Experiments show promising semantics and image retrieval results on hierarchical image databases. The results reported with non-hierarchical but categorized image databases further prove the efficacy of the proposed system. 相似文献
12.
《Information processing & management》2023,60(3):103306
Most of the existing large-scale high-dimensional streaming anomaly detection methods suffer from extremely high time and space complexity. Moreover, these models are very sensitive to parameters,make their generalization ability very low, can also be merely applied to very few specific application scenarios. This paper proposes a three-layer structure high-dimensional streaming anomaly detection model, which is called the double locality sensitive hashing Bloom filter, namely dLSHBF. We first build the former two layers that is double locality sensitive hashing (dLSH), proving that the dLSH method reduces the hash coding length of the data, and it ensures that the projected data still has a favorable mapping distance-preserving property after projection. Second, we use a Bloom filter to build the third layer of dLSHBF model, which used to improve the efficiency of anomaly detection. Six large-scale high-dimensional data stream datasets in different IIoT anomaly detection domains were selected for comparison experiments. First, extensive experiments show that the distance-preserving performance of the former dLSH algorithm proposed in this paper is significantly better than the existing LSH algorithms. Second, we verify the dLSHBF model more efficient than the other existing advanced Bloom filter model (for example Robust Bloom Filter, Fly Bloom Filter, Sandwich Learned Bloom Filter, Adaptive Learned Bloom Filters). Compared with the state of the art, dLSHBF can perform with the detection rate (DR) and false alarm rate (FAR) of anomaly detection more than 97%, and less than 2.2% respectively. Its effectiveness and generalization ability outperform other existing streaming anomaly detection methods. 相似文献
13.
针对网络邮票图像的特点,提出邮票领域本体构建方法。根据网络邮票图像的视觉特征和描述文本．利用本体描述其语义特征,通过自动图像标注技术构建邮票图像本体库,并构建网络邮票图像的语义检索系统。实验表明,该系统解决了网络图像基于关键字检索和基于内容检索中的语义缺失问题,具有较高的图像检索准确率。 相似文献
14.
【目的/意义】针对当前利用计算机管理图像资源存在图像语义特征表达不足等问题,探索和分析了特征及
特征融合对分类结果的影响,提出了一种提高图像语义分类准确率的方法。【方法/过程】本文定义了四种图像风
格,将图像描述特征划分为三个层次,探究特征融合的特点,寻求能有效表达图像语义的特征。分别采用SVM、
CNN、LSTM 及迁移学习方法实现图像风格分类,并将算法组合以提高分类效果。【结果/结论】基于迁移学习的
ResNet18模型提取的深层特征能够较好地表达图像的高级语义,将其与SVM结合能提高分类准确率。特征之间
并不总是互补,在特征选择时应避免特征冗余,造成分类效率下降。【创新/局限】本文定义的风格数目较少,且图像
展示出的风格并不绝对,往往可以被赋予多种标签,今后应进一步丰富图像数据集并尝试进行多标签分类。 相似文献
15.
This paper proposes collaborative filtering as a means to predict semantic preferences by combining information on social ties with information on links between actors and semantics. First, the authors present an overview of the most relevant collaborative filtering approaches, showing how they work and how they differ. They then compare three different collaborative filtering algorithms using articles published by New York Times journalists from 2003 to 2005 to predict preferences, where preferences refer to journalists’ inclination to use certain words in their writing. Results show that while preference profile similarities in an actor’s neighbourhood are a good predictor of her semantic preferences, information on her social network adds little to prediction accuracy. 相似文献
16.
17.
With ever increasing information being available to the end users, search engines have become the most powerful tools for obtaining useful information scattered on the Web. However, it is very common that even most renowned search engines return result sets with not so useful pages to the user. Research on semantic search aims to improve traditional information search and retrieval methods where the basic relevance criteria rely primarily on the presence of query keywords within the returned pages. This work is an attempt to explore different relevancy ranking approaches based on semantics which are considered appropriate for the retrieval of relevant information. In this paper, various pilot projects and their corresponding outcomes have been investigated based on methodologies adopted and their most distinctive characteristics towards ranking. An overview of selected approaches and their comparison by means of the classification criteria has been presented. With the help of this comparison, some common concepts and outstanding features have been identified. 相似文献
18.
The paper is concerned with similarity search at large scale, which efficiently and effectively finds similar data points for a query data point. An efficient way to accelerate similarity search is to learn hash functions. The existing approaches for learning hash functions aim to obtain low values of Hamming distances for the similar pairs. However, these methods ignore the ranking order of these Hamming distances. This leads to the poor accuracy about finding similar items for a query data point. In this paper, an algorithm is proposed, referred to top k RHS (Rank Hash Similarity), in which a ranking loss function is designed for learning a hash function. The hash function is hypothesized to be made up of l binary classifiers. The issue of learning a hash function can be formulated as a task of learning l binary classifiers. The algorithm runs l rounds and learns a binary classifier at each round. Compared with the existing approaches, the proposed method has the same order of computational complexity. Nevertheless, experiment results on three text datasets show that the proposed method obtains higher accuracy than the baselines. 相似文献
19.
In this paper, we present ViGOR (Video Grouping, Organisation and Recommendation), an exploratory video retrieval system. Exploratory video retrieval tasks are hampered by the lack of semantics associated to video and the overwhelming amount of video items stored in these types of collections (e.g. YouTube, MSN video, etc.). In order to help facilitate these exploratory video search tasks we present a system that utilises two complementary approaches: the first a new search paradigm that allows the semantic grouping of videos and the second the exploitation of past usage history in order to provide video recommendations. We present two types of recommendation techniques adapted to the grouping search paradigm: the first is a global recommendation, which couples the multi-faceted nature of explorative video retrieval tasks with the current user need of information in order to provide recommendations, and second is a local recommendation, which exploits the organisational features of ViGOR in order to provide more localised recommendations based on a specific aspect of the user task. Two user evaluations were carried out in order to (1) validate the new search paradigm provided by ViGOR, characterised by the grouping functionalities and (2) evaluate the usefulness of the proposed recommendation approaches when integrated into ViGOR. The results of our evaluations show (1) that the grouping, organisational and recommendation functionalities can result in an improvement in the users’ search performance without adversely impacting their perceptions of the system and (2) that both recommendation approaches are relevant to the users at different stages of their search, showing the importance of using multi-faceted recommendations for video retrieval systems and also illustrating the many uses of collaborative recommendations for exploratory video search tasks. 相似文献
20.
Web information retrieval and knowledge discovery are undergoing changes. The size of the Web and the heterogeneity of web pages generate new challenges in meeting user needs. This paper investigates the different methods deployed that add semantics to web content: semantic tagging and semantic APIs. The research carried out investigates existing systems in each category, outlining their primary features and functionality. It then proposes a framework for the evaluation of semantic tagging based on the main requirements for information discovery and recommends a number of comparative assessments, ranging from basic product information and requirements’ analysis to the evaluation of the APIs information modelling functionality. 相似文献