Adaptive tree similarity learning for image retrieval

Learning-enhanced relevance feedback is one of the most promising and active research directions in content-based image retrieval in recent years. However, the existing approaches either require prior knowledge of the data or converge slowly and are thus not coneffective. Motivated by the successful history of optimal adaptive filters, we present a new approach to interactive image retrieval based on an adaptive tree similarity model to solve these difficulties. The proposed tree model is a hierarchical nonlinear Boolean representation of a user query concept. Each path of the tree is a clustering pattern of the feedback samples, which is small enough and local in the feature space that it can be approximated by a linear model nicely. Because of the linearity, the parameters of the similartiy model are better learned by the optimal adaptive filter, which does not require any prior knowledge of the data and supports incremental learning with a fast convergence rate. The proposed approach is simple to implement and achieves better performance than most approaches. To illustrate the performance of the proposed approach, extensive experiments have been carried out on a large heterogeneous image collection with 17,000 images, which render promising results on a wide variety of queries.An early version of part of the system was reported in Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2001.     

文本提取和相似反馈的互联网图像检索研究

使用基于文本的互联网图像检索技术是互联网图像检索最实用的方式,也对其他方式的互联网图像检索有重要辅助作用,但如何利用周边文本来对图像进行准确描述一直是一个难题。利用TFIDF为基础提出了一个基于句法和文本重要性分类的图像关键词权重计算方法,并尝试通过图像的相似性因素作为反馈进一步优化搜索结果,为用户返回最贴切的搜索结果。     

Irrelevance reduction with locality-sensitive hash learning for efficient cross-media retrieval

Cross-media retrieval is an imperative approach to handle the explosive growth of multimodal data on the web. However, existing approaches to cross-media retrieval are computationally expensive due to high dimensionality. To efficiently retrieve in multimodal data, it is essential to reduce the proportion of irrelevant documents. In this paper, we propose a fast cross-media retrieval approach (FCMR) based on locality-sensitive hashing (LSH) and neural networks. One modality of multimodal information is projected by LSH algorithm to cluster similar objects into the same hash bucket and dissimilar objects into different ones and then another modality is mapped into these hash buckets using hash functions learned through neural networks. Once given a textual or visual query, it can be efficiently mapped to a hash bucket in which objects stored can be near neighbors of this query. Experimental results show that, in the set of the queries’ near neighbors obtained by the proposed method, the proportions of relevant documents can be much boosted, and it indicates that the retrieval based on near neighbors can be effectively conducted. Further evaluations on two public datasets demonstrate the efficacy of the proposed retrieval method compared to the baselines.     

Siamese transformer network-based similarity metric learning for cross-source remote sensing image retrieval

Neural Computing and Applications - As a fundamental technique for mining and analysis of remote sensing (RS) big data, content-based remote sensing image retrieval (CBRSIR) has received a lot of...     

Ontology-based similarity for product information retrieval

Product development of today is becoming increasingly knowledge intensive. Specifically, design teams face considerable challenges in making effective use of increasing amounts of information. In order to support product information retrieval and reuse, one approach is to use case-based reasoning (CBR) in which problems are solved “by using or adapting solutions to old problems.” In CBR, a case includes both a representation of the problem and a solution to that problem. Case-based reasoning uses similarity measures to identify cases which are more relevant to the problem to be solved. However, most non-numeric similarity measures are based on syntactic grounds, which often fail to produce good matches when confronted with the meaning associated to the words they compare. To overcome this limitation, ontologies can be used to produce similarity measures that are based on semantics. This paper presents an ontology-based approach that can determine the similarity between two classes using feature-based similarity measures that replace features with attributes. The proposed approach is evaluated against other existing similarities. Finally, the effectiveness of the proposed approach is illustrated with a case study on product–service–system design problems.     

Deep hashing with top similarity preserving for image retrieval

Multimedia Tools and Applications - Hashing has drawn more and more attention in image retrieval due to its high search speed and low storage cost. Traditional hashing methods project the...     

Combining visual dictionary, kernel-based similarity and learning strategy for image category retrieval

This paper presents a search engine architecture, RETIN, aiming at retrieving complex categories in large image databases. For indexing, a scheme based on a two-step quantization process is presented to compute visual codebooks. The similarity between images is represented in a kernel framework. Such a similarity is combined with online learning strategies motivated by recent machine-learning developments such as active learning. Additionally, an offline supervised learning is embedded in the kernel framework, offering a real opportunity to learn semantic categories. Experiments with real scenario carried out from the Corel Photo database demonstrate the efficiency and the relevance of the RETIN strategy and its outstanding performances in comparison to up-to-date strategies.     

Active learning for image retrieval with Co-SVM

In relevance feedback algorithms, selective sampling is often used to reduce the cost of labeling and explore the unlabeled data. In this paper, we proposed an active learning algorithm, Co-SVM, to improve the performance of selective sampling in image retrieval. In Co-SVM algorithm, color and texture are naturally considered as sufficient and uncorrelated views of an image. SVM classifiers are learned in color and texture feature subspaces, respectively. Then the two classifiers are used to classify the unlabeled data. These unlabeled samples which are differently classified by the two classifiers are chose to label. The experimental results show that the proposed algorithm is beneficial to image retrieval.     

Learning non-metric visual similarity for image retrieval

Measuring visual similarity between two or more instances within a data distribution is a fundamental task in image retrieval. Theoretically, non-metric distances are able to generate a more complex and accurate similarity model than metric distances, provided that the non-linear data distribution is precisely captured by the system. In this work, we explore neural networks models for learning a non-metric similarity function for instance search. We argue that non-metric similarity functions based on neural networks can build a better model of human visual perception than standard metric distances. As our proposed similarity function is differentiable, we explore a real end-to-end trainable approach for image retrieval, i.e. we learn the weights from the input image pixels to the final similarity score. Experimental evaluation shows that non-metric similarity networks are able to learn visual similarities between images and improve performance on top of state-of-the-art image representations, boosting results in standard image retrieval datasets with respect standard metric distances.     

互信息启发的相似度组合图像检索算法

图像的视觉特征与用户描述之间的差距一直是影响基于内容的图像检索准确度的最主要因素。对多种相似度进行组合来检索图像是近几年图像检索领域涌现出的一个研究热点,也是缩小这种差距的一种有效途径。如何选择更好的组合方法则是该领域很多研究者关注的核心问题。提出一种新的相似度组合算法。该算法基于互信息度量相对熵的原理,计算连续变量相似度与离散变量相似性之间的相关性,对多种相似度进行选择,以“和规则”组合相似度。在公用数据集上进行检索实验,该算法优于当前其他的“和规则”下的组合方法。     

Learning similarity measure for natural image retrieval with relevance feedback

A new scheme of learning similarity measure is proposed for content-based image retrieval (CBIR). It learns a boundary that separates the images in the database into two clusters. Images inside the boundary are ranked by their Euclidean distances to the query. The scheme is called constrained similarity measure (CSM), which not only takes into consideration the perceptual similarity between images, but also significantly improves the retrieval performance of the Euclidean distance measure. Two techniques, support vector machine (SVM) and AdaBoost from machine learning, are utilized to learn the boundary. They are compared to see their differences in boundary learning. The positive and negative examples used to learn the boundary are provided by the user with relevance feedback. The CSM metric is evaluated in a large database of 10009 natural images with an accurate ground truth. Experimental results demonstrate the usefulness and effectiveness of the proposed similarity measure for image retrieval.     

A short-term learning approach based on similarity refinement in content-based image retrieval

Multimedia Tools and Applications - This paper presents a new relevance feedback approach based on similarity refinement. In the proposed approach weight correction of feature’s components is...     

Virtual images for similarity retrieval in image databases

We introduce the virtual image, an iconic index suited for pictorial information access in a pictorial database, and a similarity retrieval approach based on virtual images to perform content-based retrieval. A virtual image represents the spatial information contained in a real image in explicit form by means of a set of spatial relations. This is useful to efficiently compute the similarity between a query and an image in the database. We also show that virtual images support real-world applications that require translation, reflection, and/or rotation invariance of image representation     

Exploring contextual information for view-wised 3D model retrieval

Multimedia Tools and Applications - Recently, with the rapid development of digital technologies and its wide application, 3D model retrieval is becoming more and more important in graphic...     

Supervised Coarse-to-Fine Semantic Hashing for cross-media retrieval

Due to its storage efficiency and fast query speed, cross-media hashing methods have attracted much attention for retrieving semantically similar data over heterogeneous datasets. Supervised hashing methods, which utilize the labeled information to promote the quality of hashing functions, achieve promising performance. However, the existing supervised methods generally focus on utilizing coarse semantic information between samples (e.g. similar or dissimilar), and ignore fine semantic information between samples which may degrade the quality of hashing functions. Accordingly, in this paper, we propose a supervised hashing method for cross-media retrieval which utilizes the coarse-to-fine semantic similarity to learn a sharing space. The inter-category and intra-category semantic similarity are effectively preserved in the sharing space. Then an iterative descent scheme is proposed to achieve an optimal relaxed solution, and hashing codes can be generated by quantizing the relaxed solution. At last, to further improve the discrimination of hashing codes, an orthogonal rotation matrix is learned by minimizing the quantization loss while preserving the optimality of the relaxed solution. Extensive experiments on widely used Wiki and NUS-WIDE datasets demonstrate that the proposed method outperforms the existing methods.     

Reasoning about pictures and similarity retrieval for image information systems based on SK-set knowledge representation

Spatial reasoning and similarity retrieval are two important functions of any image information system. Good spatial knowledge representation for images is necessary to adequately support these two functions. In this paper, we propose a new spatial knowledge representation, called the SK-set based on morphological skeleton theories. Spatial reasoning algorithms which achieve more accurate results by directly analysing skeletons are described. SK-set facilitates browsing and progressive visualization. We also define four new types of similarity measures and propose a similarity retrieval algorithm for performing image retrieval. Moreover, using SK-set as a spatial knowledge representation will reduce the storage space required by an image database significantly.     

图像检索中结合文本信息的多示例原型选择及主动学习策略

针对基于区域的图像检索系统检索精度不高的问题,提出结合文本信息的多示例原型选择算法和反馈标注机制。在示例原型选择时,首先使用文本信息进行正例拓展,然后通过估计负示例分布进行最初示例选择,最后通过示例更新和分类器学习的交替优化获得真的示例原型。相关反馈采用了多策略相结合的主动学习机制,通过信息值控制主动学习策略的自动切换,使系统能够自动选择当前最适合的主动学习策略。实验结果表明,该方法有效且性能优于其他方法。     

WALRUS: a similarity retrieval algorithm for image databases

Approaches for content-based image querying typically extract a single signature from each image based on color, texture, or shape features. The images returned as the query result are then the ones whose signatures are closest to the signature of the query image. While efficient for simple images, such methods do not work well for complex scenes since they fail to retrieve images that match the query only partially, that is, only certain regions of the image match. This inefficiency leads to the discarding of images that may be semantically very similar to the query image since they may contain the same objects. The problem becomes even more apparent when we consider scaled or translated versions of the similar objects. We propose WALRUS (wavelet-based retrieval of user-specified scenes), a novel similarity retrieval algorithm that is robust to scaling and translation of objects within an image. WALRUS employs a novel similarity model in which each image is first decomposed into its regions and the similarity measure between a pair of images is then defined to be the fraction of the area of the two images covered by matching regions from the images. In order to extract regions for an image, WALRUS considers sliding windows of varying sizes and then clusters them based on the proximity of their signatures. An efficient dynamic programming algorithm is used to compute wavelet-based signatures for the sliding windows. Experimental results on real-life data sets corroborate the effectiveness of WALRUS'S similarity model.     

Semantic similarity measures for enhancing information retrieval in folksonomies

Collaborative tagging systems, also known as folksonomies, enable a user to annotate various web resources with a free set of tags for sharing and searching purposes. Tags in a folksonomy reflect users’ collaborative cognition about information. Tags play an important role in a folksonomy as a means of indexing information to facilitate search and navigation of resources. However, the semantics of the tags, and therefore the semantics of the resources, are neither known nor explicitly stated. It is therefore difficult for users to find related resources due to the absence of a consistent semantic meaning among tags. The shortage of relevant tags increases data sparseness and decreases the rate of information extraction with respect to user queries. Defining semantic relationships between tags, resources, and users is an important research issue for the retrieval of related information from folksonomies. In this research, a method for finding semantic relationships among tags is proposed. The present study considers not only the pairwise relationships between tags, resources, and users, but also the relationships among all three. Experimental results using real datasets from Flickr and Del.icio.us show that the method proposed here is more effective than previous methods such as LCH, JCN, and LIN in finding semantic relationships among tags in a folksonomy.