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1.
As an information medium, video offers many possible retrieval and browsing modalities, far more than text, image or audio. Some of these, like searching the text of the spoken dialogue, are well developed, others like keyframe browsing tools are in their infancy, and others not yet technically achievable. For those modalities for browsing and retrieval which we cannot yet achieve we can only speculate as to how useful they will actually be, but we do not know for sure. In our work we have created a system to support multiple modalities for video browsing and retrieval including text search through the spoken dialogue, image matching against shot keyframes and object matching against segmented video objects. For the last of these, automatic segmentation and tracking of video objects is a computationally demanding problem which is not yet solved for generic natural video material, and when it is then it is expected to open up possibilities for user interaction with objects in video, including searching and browsing. In this paper we achieve object segmentation by working in a closed domain of animated cartoons. We describe an interactive user experiment on a medium-sized corpus of video where we were able to measure users’ use of video objects versus other modes of retrieval during multiple-iteration searching. Results of this experiment show that although object searching is used far less than text searching in the first iteration of a user’s search it is a popular and useful search type once an initial set of relevant shots have been found. 相似文献
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按照人们对产品的健康影响、环境污染方面的情感认知,开发用于评估产品环境形象的人工智能方法。基于“舆情环境形象”概念,从健康影响、环境污染、环境风险和情感倾向4个方面建立产品舆情环境形象评估框架,以产品的互联网传播大数据为信息源,人工标注分类语料,采用自然语言处理技术和卷积神经网络算法训练验证分类模型。进一步以33种高风险和高污染产品为分析对象,利用从互联网获取的相关新闻、评论或公众言论进行模型分类和环境形象评估,结果表明环境舆情判定模型F值为0.91,产品的环境情感极性均以正面情绪为主,受舆情讨论热点程度影响,舆情数量多的产品情感倾向占比趋于均衡化、舆情数量少的产品情感倾向占比趋于极端化。其中,化妆品、药品的环境健康风险最高,高环境风险产品分别包括凡士林、角鲨烯和咖啡因。 相似文献
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Chengsen Ru Jintao Tang Shasha Li Songxian Xie Ting Wang 《Information processing & management》2018,54(4):593-608
Distant supervision (DS) has the advantage of automatically generating large amounts of labelled training data and has been widely used for relation extraction. However, there are usually many wrong labels in the automatically labelled data in distant supervision (Riedel, Yao, & McCallum, 2010). This paper presents a novel method to reduce the wrong labels. The proposed method uses the semantic Jaccard with word embedding to measure the semantic similarity between the relation phrase in the knowledge base and the dependency phrases between two entities in a sentence to filter the wrong labels. In the process of reducing wrong labels, the semantic Jaccard algorithm selects a core dependency phrase to represent the candidate relation in a sentence, which can capture features for relation classification and avoid the negative impact from irrelevant term sequences that previous neural network models of relation extraction often suffer. In the process of relation classification, the core dependency phrases are also used as the input of a convolutional neural network (CNN) for relation classification. The experimental results show that compared with the methods using original DS data, the methods using filtered DS data performed much better in relation extraction. It indicates that the semantic similarity based method is effective in reducing wrong labels. The relation extraction performance of the CNN model using the core dependency phrases as input is the best of all, which indicates that using the core dependency phrases as input of CNN is enough to capture the features for relation classification and could avoid negative impact from irrelevant terms. 相似文献
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Many of the approaches to image retrieval on the Web have their basis in text retrieval. However, when searchers are asked to describe their image needs, the resulting query is often short and potentially ambiguous. The solution we propose is to perform automatic query expansion using Wikipedia as the source knowledge base, resulting in a diversification of the search results. The outcome is a broad range of images that represent the various possible interpretations of the query. In order to assist the searcher in finding images that match their specific intentions for the query, we have developed an image organization method that uses both the conceptual information associated with each image, and the visual features extracted from the images. This, coupled with a hierarchical organization of the concepts, provides an interactive interface that takes advantage of the searchers’ abilities to recognize relevant concepts, filter and focus the search results based on these concepts, and visually identify relevant images while navigating within the image space. In this paper, we outline the key features of our image retrieval system (CIDER), and present the results of a preliminary user evaluation. The results of this study illustrate the potential benefits that CIDER can provide for searchers conducting image retrieval tasks. 相似文献
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《Information processing & management》2022,59(2):102840
Scene segmentation is a very challenging task where convolutional neural networks are used in this field and have achieved very good results. Current scene segmentation methods often ignore the internal consistency of the target object, and lack to make full use of global and local context information which leads to the situation of object misclassification. In addition, most of the previous work focused on the segmentation of the main part of the object, however, there are few researches on the quality of the object edge segmentation. In this article, based on the use of flow information to maintain body consistency, the context feature extraction module is designed to fully consider the global and local body context information of the target object, refining the rough feature map in the intermediate stage. So, the misclassification of the target object is reduced. Besides, in the proposed edge attention module, the low-level feature map guided by the global feature and the edge feature map with semantic information obtained by intermediate process are connected to obtain more accurate edge detail information. Finally, the segmentation quality that contains the body part of the noise and the edge details can be improved. This paper not only conducts experiments on the classic FCN, PSPNet, and DeepLabv3+ several mainstream network architectures, but also on the real-time SFNet network structure proposed last year, and the value of mIoU in object and boundary is improved to verify the effectiveness of the method proposed in this paper. Moreover, in order to prove the robustness of the experiment, we conduct experiments on three complex scene segmentation data sets of Cityscapes, CamVid, and KiTTi, and obtained mIoU values of 80.52% on the Cityscapes validation data set, and 71.4%, 56.53% on the Camvid and KITTI test data set, which shows better results when compared with most of the state-of-the-art methods. 相似文献
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《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
8.
Basant Agarwal Heri Ramampiaro Helge Langseth Massimiliano Ruocco 《Information processing & management》2018,54(6):922-937
This paper is concerned with paraphrase detection, i.e., identifying sentences that are semantically identical. The ability to detect similar sentences written in natural language is crucial for several applications, such as text mining, text summarization, plagiarism detection, authorship authentication and question answering. Recognizing this importance, we study in particular how to address the challenges with detecting paraphrases in user generated short texts, such as Twitter, which often contain language irregularity and noise, and do not necessarily contain as much semantic information as longer clean texts. We propose a novel deep neural network-based approach that relies on coarse-grained sentence modelling using a convolutional neural network (CNN) and a recurrent neural network (RNN) model, combined with a specific fine-grained word-level similarity matching model. More specifically, we develop a new architecture, called DeepParaphrase, which enables to create an informative semantic representation of each sentence by (1) using CNN to extract the local region information in form of important n-grams from the sentence, and (2) applying RNN to capture the long-term dependency information. In addition, we perform a comparative study on state-of-the-art approaches within paraphrase detection. An important insight from this study is that existing paraphrase approaches perform well when applied on clean texts, but they do not necessarily deliver good performance against noisy texts, and vice versa. In contrast, our evaluation has shown that the proposed DeepParaphrase-based approach achieves good results in both types of texts, thus making it more robust and generic than the existing approaches. 相似文献
9.
《Information processing & management》2020,57(6):102306
How to parse the human image to obtain the text label corresponding to the human body is a critical task for human-computer interaction. Although previous methods have significantly improved the parsing performance, the problem of parsing confusion and tiny target missing remains unresolved, which leads to errors and incomplete inference accordingly. Targeting at these drawbacks, we fuse semantic and spatial features to mine the human body information based on the Dual Pyramid Unit convolutional neural network, named as DPUNet. DPUNet is composed of Context Pyramid Unit (CPU) and Spatial Pyramid Unit (SPU). Firstly, we design the CPU to aggregate the local to global semantic information, which exports the semantic feature for eliminating the semantic confusion. To capture the tiny targets for preventing the details from missing, the SPU is proposed to incorporate the multi-scale spatial information and output the spatial feature. Finally, the features of two complementary units are fused for accurate and complete human parsing results. Our approach achieves more excellent performance than the state-of-the-art methods on single human and multiple human parsing datasets. Meanwhile, the proposed framework is efficient with a fast speed of 41.2fps. 相似文献
10.
Peretz Shoval 《Information processing & management》1985,21(6):475-487
An expert system was developed in the area of information retrieval, with the objective of performing the job of an information specialist, who assists users in selecting the right vocabulary terms for a database search.The system is composed of two components: One is the knowledge base, represented as a semantic network, in which the nodes are words, concepts, phrases, comprising a vocabulary of the application area and the links express semantic relationships between those nodes. The second component is the rules, or procedures, which operate upon the knowledge-base, analogous to the decision rules or work patterns of the information specialist.Two major stages comprise the consulting process of the system: During the “search” stage relevant knowledge in the semantic network is activated, and search and evaluation rules are applied in order to find appropriate vocabulary terms to represent the user's problem. During the “suggest” stage those terms are further evaluated, dynamically rank-ordered according to relevancy, and suggested to the user. Explanations to the findings can be provided by the system and backtracking is possible in order to find alternatives in case some suggested term is rejected by the user.This article presents the principle, procedures and rules which are utilized in the expert system. 相似文献
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[目的/意义]在非结构化语料集中抽取知识要素,是实现知识图谱的重要环节,本文探索了应用深度学习中的卷积神经网络（CNN）模型进行旅游领域知识关系抽取方法。[方法/过程]抓取专业旅游网站的相关数据建立语料库,对部分语料进行人工标注作为训练集和测试集,通过Python语言编程实现分词、向量化及CNN模型,进行关系抽取实验。[结果/结论]实验结果表明,应用卷积神经网络对非结构化的旅游文本进行关系抽取时能够取得满意的效果（Precision 0.77,Recall 0.76,F1-measure 0.76）。抽取结果通过人工校对进行优化后,可以为旅游知识图谱构建、领域本体构建等工作奠定基础。 相似文献
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《Journal of The Franklin Institute》2023,360(11):7438-7456
Hybrid quantum-classical algorithms provide a promising way to harness the power of current quantum devices. In this framework, parametrized quantum circuits (PQCs) which consist of layers of parametrized unitaries can be considered as a kind of quantum neural networks. Recent works have begun to explore the potential of PQCs as general function approximators. In this work, we propose a quantum-classical deep network structure to enhance model discriminability of convolutional neural networks (CNNs). In CNNs, the convolutional layer uses linear filters to scan the input data followed by a nonlinear operation. Instead, we build PQCs, which are more potent function approximators, with more complex structures to capture the features within the receptive field. The feature maps are obtained by sliding the PQCs over the input in a similar way as CNN. We also give a training algorithm for the proposed model. Through numerical simulation, the proposed hybrid models demonstrate reasonable classification performance on MNIST and Fashion-MNIST (4-classes). In addition, we compare the performance of models in different settings. The results demonstrate that the model with high-expressibility ansaetze achieves lower cost and higher accuracy, but exhibits a “saturation” phenomenon. 相似文献
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《Information processing & management》2022,59(1):102726
In synthetic aperture radar (SAR) image change detection, the deep learning has attracted increasingly more attention because the difference images (DIs) of traditional unsupervised technology are vulnerable to speckle noise. However, most of the existing deep networks do not constrain the distributional characteristics of the hidden space, which may affect the feature representation performance. This paper proposes a variational autoencoder (VAE) network with the siamese structure to detect changes in SAR images. The VAE encodes the input as a probability distribution in the hidden space to obtain regular hidden layer features with a good representation ability. Furthermore, subnetworks with the same parameters and structure can extract the spatial consistency features of the original image, which is conducive to the subsequent classification. The proposed method includes three main steps. First, the training samples are selected based on the false labels generated by a clustering algorithm. Then, we train the proposed model with the semisupervised learning strategy, including unsupervised feature learning and supervised network fine-tuning. Finally, input the original data instead of the DIs in the trained network to obtain the change detection results. The experimental results on four real SAR datasets show the effectiveness and robustness of the proposed method. 相似文献
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《Information processing & management》2020,57(6):102352
Intracerebral hemorrhage (ICH) is the most serious type of stroke, which results in a high disability or mortality rate. Therefore, accurate and rapid ICH region segmentation is of great significance for clinical diagnosis and treatment of ICH. In this paper, we focus on deep neural networks to automatically segment ICH regions. Firstly, we propose an encoder-decoder convolutional neural network (ED-Net) architecture to comprehensively utilizing both the low-level and high-level semantic information. Specifically, the encoder is used to extract multi-scale semantic feature information, while the decoder integrates them to form a unified ICH feature representation. Secondly, we introduce a synthetic loss function by paying more attention to the small ICH regions to overcome the data imbalanced problem. Thirdly, to improve the clinical adaptability of the proposed model, we collect 480 patient cases with ICH from four hospitals to construct a multi-center dataset, in which each case contains the first and review CT scans. In particular, CT scans of different patients are diverse, which greatly increases the difficulty of segmentation. Finally, we evaluate ED-Net on the multi-center ICH clinical dataset from different model parameters and different loss functions. We also compare the results of ED-Net with nine state-of-the-art methods in the literature. Both quantitative and visual results have shown that ED-Net outperforms other methods by providing more accurate and stable performance. 相似文献
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《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
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Internet已成为全球最丰富的数据源,数据类型繁杂且动态变化,如何从中快速准确地检索出用户所需要的信息是一个亟待解决的问题.传统的搜索引擎基于语法的方式进行搜索,缺乏语义信息,难以准确地表达用户的查询需求和被检索对象的文档语义,致使查准率和查全率较低且搜索范围有限.本文对现有的语义检索方法进行了研究,分析了其中存在的问题,在此基础上提出了一种基于领域的语义搜索引擎模型,结合语义Web技术,使用领域本体元数据模型对用户的查询进行语义化规范,依据领域本体模式抽取文档中的知识并RDF化,准确地表达了用户的查询语义和作为被查询对象的文档语义,可以大大提高检索的准确性和检索效率,详细地给出了模型的体系结构、基本功能和工作原理. 相似文献
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Most existing search engines focus on document retrieval. However, information needs are certainly not limited to finding relevant documents. Instead, a user may want to find relevant entities such as persons and organizations. In this paper, we study the problem of related entity finding. Our goal is to rank entities based on their relevance to a structured query, which specifies an input entity, the type of related entities and the relation between the input and related entities. We first discuss a general probabilistic framework, derive six possible retrieval models to rank the related entities, and then compare these models both analytically and empirically. To further improve performance, we study the problem of feedback in the context of related entity finding. Specifically, we propose a mixture model based feedback method that can utilize the pseudo feedback entities to estimate an enriched model for the relation between the input and related entities. Experimental results over two standard TREC collections show that the derived relation generation model combined with a relation feedback method performs better than other models. 相似文献
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Content-based image retrieval for medical images is a primary technique for computer-aided diagnosis. While it is a premise for computer-aided diagnosis system to build an efficient medical image database which is paid less attention than that it deserves. In this paper, we provide an efficient approach to develop the archives of large brain CT medical data. Medical images are securely acquired along with relevant diagnosis reports and then cleansed, validated and enhanced. Then some sophisticated image processing algorithms including image normalization and registration are applied to make sure that only corresponding anatomy regions could be compared in image matching. A vector of features is extracted by non-negative tensor factorization and associated with each image, which is essential for the content-based image retrieval. Our experiments prove the efficiency and promising prospect of this database building method for computer-aided diagnosis system. The brain CT image database we built could provide radiologists with a convenient access to retrieve pre-diagnosed, validated and highly relevant examples based on image content and obtain computer-aided diagnosis. 相似文献
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语义Web技术的广泛研究为社会网络分析方法提供了新的思路,已经成为社会网络分析领域研究的重点问题之一。本文以语义Web技术在社会网络分析中的应用为研究对象,首先回顾了国内外学者在语义Web技术与社会网络分析交叉研究中的成果,然后对社会语义网络进行了分析,并提出了基于语义Web的社会网络分析应用模型,最后指出了该模型实现的技术难点和下一步的研究重点。 相似文献