Improving image retrieval by using spatial relations

In this paper we proposed the use of spatial relations as a way of improving annotation-based image retrieval. We analyzed different types of spatial relations and selected the most adequate ones for image retrieval. We developed an image comparison and retrieval method based on conceptual graphs, which incorporates spatial relations. Additionally, we proposed an alternative term-weighting scheme and explored the use of more than one sample image for retrieval using several late fusion techniques. Our methods were evaluated with a rich and complex image dataset, based on the 39 topics developed for the ImageCLEF 2008 photo retrieval task. Results show that: (i) incorporating spatial relations produces a significant increase in performance, (ii) the label weighting scheme we proposed obtains better results than other traditional schemes, and (iii) the combination of several sample images using late fusion produces an additional improvement in retrieval according to several metrics.     

Approximate retrieval of high-dimensional data withL 1 metric by spatial indexing

High-dimensional data, such as documents, digital images, and audio clips, can be considered as spatial objects, which induce a metric space where the metric can be used to measure dissimilarities between objects. We investigate a method for retrieving objects within some distance from a given object by utilizing a spatial indexing/access method R-tree, which usually assumes Euclidean metric. First, we prove that objects in discreteL ₁ (or Manhattan distance) metric space can be embedded into vertices of a unit hyper-cube in Euclidean space when the square root ofL ₁ distance is used as the distance. To take fully advantage of R-tree spatial indexing, we have to project objects into space of relatively lower dimension. We adopt FastMap by Faloutsos and Lin to reduce the dimension of object space. The range corresponding to a query (Q, h) for retrieving objects within distanceh from a objectQ is naturally considered as a hyper-sphere even after FastMap projection, which is an orthogonal projection in Euclidean space. However, it is turned out that the query range is contracted into a smaller hyper-box than the hyper-sphere by applying FastMap to objects embedded in the above mentioned way. Finally, we give a brief summary of experiments in applying our method to Japanese chess boards. Takeshi Shinohara, Dr.Sci.: He is a Professor in the Department of Artificial Intelligence at Kyushu Institute of Technology. He obtained his bachelors degree in Mathematics from Kyoto University in 1980, and his Dr. Sci. from Kyushu University in 1986. His research interests are in Computational/Algorithmic Learning Theory, Information Retrieval, and Approximate Retrieval of Multimedia Data. Hiroki Ishizaka, Dr.Sci.: He is an Associate Professor in the Department of Artificial Intelligence at Kyushu Institute of Technology. He obtained his bachelors degree in Mathematics from Kyushu University in 1984, and his Dr.Sci. from Kyushu University in 1993. His research interests are in Computational/Algorithmic Learning Theory.     

Symbolic image indexing and retrieval by spatial similarity: An approach based on B-tree

In this paper, the problem of indexing symbolic images based on spatial similarity is addressed. A model based on modified triangular spatial relationship (TSR) and B-tree is proposed. The model preserves TSR among the components in a symbolic image by the use of quadruples. A Symbolic Image Database (SID) is created through the construction of B-tree, an efficient multilevel indexing structure. A methodology to retrieve similar symbolic images for a given query image is also presented. The presented retrieval model has logarithmic search time complexity. The study made in this work reveals that the model bears various advantages when compared to other existing models and it could be extended towards dynamic databases. An extensive experimentation is conducted on various symbolic images and also on the ORL and YALE face databases. The results of the experimentation conducted have revealed that the proposed scheme outperforms the existing algorithms and is of practical relevance.     

基于空间关系的维吾尔文图像关键词检索

为提高维吾尔文档图像的检索效率,提出一种基于字符空间关系的关键词检索方法.通过对文档图像进行单词切分,提取切分后单词图像的字符空间位置特征,将提取的特征根据单词的连体段数目存储为多个特征文件,根据输入关键词图像的特征寻找对应的特征文件进行查询.从115张印刷体维吾尔文档图像切分后的24460张单词集中选取10张有丰富含...     

Searching in metric spaces by spatial approximation

We propose a new data structure to search in metric spaces. A metric space is formed by a collection of objects and a distance function defined among them which satisfies the triangle inequality. The goal is, given a set of objects and a query, retrieve those objects close enough to the query. The complexity measure is the number of distances computed to achieve this goal. Our data structure, called sa-tree (“spatial approximation tree”), is based on approaching the searched objects spatially, that is, getting closer and closer to them, rather than the classic divide-and-conquer approach of other data structures. We analyze our method and show that the number of distance evaluations to search among n objects is sublinear. We show experimentally that the sa-tree is the best existing technique when the metric space is hard to search or the query has low selectivity. These are the most important unsolved cases in real applications. As a practical advantage, our data structure is one of the few that does not need to tune parameters, which makes it appealing for use by non-experts. Edited by R. Sacks-Davis Received: 17 April 2001 / Accepted: 24 January 2002 / Published online: 14 May 2002     

Fast gradual matching measure for image retrieval based on visual similarity and spatial relations

In this article, we propose a new method to retrieve images containing a request set of regions. The user is asked to specify a set of regions belonging to a single image. Then this request set of regions is compared to the sets of the regions of the images in the database. We propose a comparison measure that not only evaluates the similarity of regions one to the other, but that also takes into account the spatial configuration of the regions. The spatial structure of the regions is represented by means of fuzzy spatial relations, like horizontal and vertical disposal and connexity. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 711–723, 2006.     

Case-based reasoning classifier based on learning pseudo metric retrieval

In case-based reasoning (CBR) classification systems, the similarity metrics play a key role and directly affect the system's performance. Based on our previous work on the learning pseudo metrics (LPM), we propose a case-based reasoning method for pattern classification, where the widely used Euclidean distance is replaced by the LPM to measure the closeness between the target case and each source case. The same type of case as the target case can be retrieved and the category of the target case can be defined by using the majority of reuse principle. Experimental results over some benchmark datasets and a fault diagnosis of the Tennessee-Eastman (TE) process demonstrate that the proposed reasoning techniques in this paper can effectively improve the classification accuracy, and the LPM-based retrieval method can substantially improve the quality and learning ability of CBR classifiers.     

Eilenberg–Moore algebras for stochastic relations

We investigate the category of Eilenberg–Moore algebras for the Giry monad associated with stochastic relations over Polish spaces with continuous maps as morphisms. The algebras are identified as the positive convex structures on the base space. The forgetful functor assigning a positive convex structure the underlying Polish space has the stochastic powerdomain as its left adjoint.     

Weighted walkthroughs between extended entities for retrieval by spatial arrangement

In the access to image databases, queries based on the appearing visual features of searched data reduce the gap between the user and the engineering representation. To support this access modality, image content can be modeled in terms of different types of features such as shape, texture, color, and spatial arrangement. An original framework is presented which supports quantitative nonsymbolic representation and comparison of the mutual positioning of extended nonrectangular spatial entities. Properties of the model are expounded to develop an efficient computation technique and to motivate and assess a metric of similarity for quantitative comparison of spatial relationships. Representation and comparison of binary relationships between entities is then embedded into a graph-theoretical framework supporting representation and comparison of the spatial arrangements of a picture. Two prototype applications are described.     

Θℜ-string: A geometry-based representation for efficientand effective retrieval of images by spatial similarity

A spatial similarity algorithm assesses the degree to which the spatial relationships among the domain objects in a database image conform to those specified in the query image. In this paper, we propose a geometry-based structure for representing the spatial relationships in the images and an associated spatial similarity algorithm. The proposed algorithm recognizes both translation, scale, and rotation variants of an image, and variants of the image generated by an arbitrary composition of translation, scale, and rotation transformations. The algorithm has Θ(n log n) time complexity in terms of the number of objects common to the database and query images. The retrieval effectiveness of the proposed algorithm is evaluated using the TESSA image collection     

PicSOM – content-based image retrieval with self-organizing maps

We have developed a novel system for content-based image retrieval in large, unannotated databases. The system is called PicSOM, and it is based on tree structured self-organizing maps (TS-SOMs). Given a set of reference images, PicSOM is able to retrieve another set of images which are similar to the given ones. Each TS-SOM is formed with a different image feature representation like color, texture, or shape. A new technique introduced in PicSOM facilitates automatic combination of responses from multiple TS-SOMs and their hierarchical levels. This mechanism adapts to the user's preferences in selecting which images resemble each other. Thus, the mechanism implements a relevance feedback technique on content-based image retrieval. The image queries are performed through the World Wide Web and the queries are iteratively refined as the system exposes more images to the user.     

Multimedia news exploration and retrieval by integrating keywords,relations and visual features

Multimedia news may be organized by the keywords and categories for exploration and retrieval applications, but it is very difficult to integrate the relation and visual information into the traditional category browsing and keyword-based search framework. This paper propose a new semantic model that can integrate keyword, relation and visual information in a uniform framework. Based on this semantic representation framework, the news exploration and retrieval applications can be organized by not only keywords and categories but also relations and visual properties. We also proposed a set of algorithms to automatically extract the proposed semantic model automatically from large collection of multimedia news reports.     

Improving retrieval accuracy of Hierarchical Cellular Trees for generic metric spaces

Metric Access Methods (MAMs) are indexing techniques which allow working in generic metric spaces. Therefore, MAMs are specially useful for Content-Based Image Retrieval systems based on features which use non L _p norms as similarity measures. MAMs naturally allow the design of image browsers due to their inherent hierarchical structure. The Hierarchical Cellular Tree (HCT), a MAM-based indexing technique, provides the starting point of our work. In this paper, we describe some limitations detected in the original formulation of the HCT and propose some modifications to both the index building and the search algorithm. First, the covering radius, which is defined as the distance from the representative to the furthest element in a node, may not cover all the elements belonging to the node’s subtree. Therefore, we propose to redefine the covering radius as the distance from the representative to the furthest element in the node’s subtree. This new definition is essential to guarantee a correct construction of the HCT. Second, the proposed Progressive Query retrieval scheme can be redesigned to perform the nearest neighbor operation in a more efficient way. We propose a new retrieval scheme which takes advantage of the benefits of the search algorithm used in the index building. Furthermore, while the evaluation of the HCT in the original work was only subjective, we propose an objective evaluation based on two aspects which are crucial in any approximate search algorithm: the retrieval time and the retrieval accuracy. Finally, we illustrate the usefulness of the proposal by presenting some actual applications.     

Knowledge-based image retrieval with spatial and temporalconstructs

A knowledge-based approach to retrieve medical images by feature and content with spatial and temporal constructs is developed. Selected objects of interest in an image are segmented and contours are generated. Features and content are extracted and stored in a database. Knowledge about image features can be expressed as a type abstraction hierarchy (TAH), the high-level nodes of which represent the most general concepts. Traversing TAH nodes allows approximate matching by feature and content if an exact match is not available. TAHs can be generated automatically by clustering algorithms based on feature values in the databases and hence are scalable to large collections of image features. Since TAHs are generated based on user classes and applications, they are context- and user-sensitive. A knowledge-based semantic image model is proposed to represent the various aspects of an image object's characteristics. The model provides a mechanism for accessing and processing spatial, evolutionary and temporal queries. A knowledge-based spatial temporal query language (KSTL) has been developed that extends ODMG's OQL and supports approximate matching of features and content, conceptual terms and temporal logic predicates. Further, a visual query language has been developed that accepts point-click-and-drag visual iconic input on the screen that is then translated into KSTL. User models are introduced to provide default parameter values for specifying query conditions. We have implemented the KMeD (Knowledge-based Medical Database) system using these concepts     

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...     

Taxonomies of logically defined qualitative spatial relations

This paper develops a taxonomy of qualitative spatial relations for pairs of regions, which are all logically defined from two primitive (but axiomatized) notions. The first primitive is the notion of two regions being connected, which allows eight jointly exhaustive and pairwise disjoint relations to be defined. The second primitive is the convex hull of a region which allows many more relations to be defined. We also consider the development of the useful notions of composition tables for the defined relations and networks specifying continuous transitions between pairs of regions. We conclude by discussing what kind of criteria to apply when deciding how fine grained a taxonomy to create.     

Integration of fuzzy spatial relations in deformable models—Application to brain MRI segmentation

This paper presents a general framework to integrate a new type of constraints, based on spatial relations, in deformable models. In the proposed approach, spatial relations are represented as fuzzy subsets of the image space and incorporated in the deformable model as a new external force. Three methods to construct an external force from a fuzzy set representing a spatial relation are introduced and discussed. This framework is then used to segment brain subcortical structures in magnetic resonance images (MRI). A training step is proposed to estimate the main parameters defining the relations. The results demonstrate that the introduction of spatial relations in a deformable model can substantially improve the segmentation of structures with low contrast and ill-defined boundaries.     

基于真实物体模型的空间对象检索技术研究

针对MBR模型只适合检索联通的空间对象且查准率较低的问题,建立了真实物体模型,依据空间对象在平面上的几何投影的方法建立真实物体模型,采用深度方向关系矩阵表示真实物体模型之间的方向关系,利用该模型可以很好地将矩形代数良好的计算性质应用于空间对象检索技术中,简化了检索的过程,实现了矩形代数与真实物体模型的有效结合,使其检索的范围变的更加广泛.同时提出了一种改进的R*树算法,有效地提高了真实物体模型的检索精确度,降低了错误匹配的发生率.实验结果表明,该算法可以有效地提高检索的查准率,提高空间对象检索的效率.     

Research intelligence involving information retrieval – An example of conferences and journals

This paper reports a work that was intended to reveal the connection between topics investigated by conference papers and journal papers. This work selected hundreds of papers in data mining and information retrieval from well-known databases and showed that the topics covered by conference papers in a year often leads to similar topics covered by journal papers in the subsequent year and vice versa. This study used some existing algorithms and combination of these algorithms to proposed a new detective procedure for the researchers to detect the new trend and get the academic intelligence from conferences and journals.The goal of this research is fourfold: First, the research investigates if the conference papers’ themes lead the journal papers’. Second, the research examines how the new research themes can be identified from the conference papers. Third, the research looks at a specific area such as information retrieval and data mining as an illustration. Fourth, the research studies any inconsistencies of the correlation between the conference papers and the journal papers.This study explores the connections between the academic publications. The methodologies of information retrieval and data mining can be exploited to discover the relationships between published papers among all topics. By discovering the connections between conference papers and journal papers, researchers can improve the effectiveness of their research by identifying academic intelligence.This study discusses how conference papers and journal papers are related. The topics of conference papers are identified to determine whether they represent new trend discussed in journal papers. An automatic examination procedure based on information retrieval and data mining is also proposed to minimize the time and human resources required to predict further research developments. This study develops a new procedure and collects a dataset to verify those problems. Analytical results demonstrate that the conference papers submitted to journals papers are similar each year. Conference papers certainly affect the journal papers published over three years. About 87.23% of data points from papers published in 1991–2007 support our assumption. The research is intended to help researchers identify new trend in their research fields, and focus on the urgent topics. This is particularly valuable for new researchers in their field, or those who wish to perform cross-domain studies.     

Vocabulary development at home: a multimedia elaborated picture supporting parent–toddler interaction

Some children enter elementary school with large vocabulary delays, which negatively influence their later school performance. A rich home language environment can support vocabulary development through frequent high‐quality parent–toddler interaction. Elaborated picture home activities can support this rich home language environment. This study compares the effects of a multimedia versus a paper elaborated picture on the parent–toddler interaction and toddlers' vocabulary development. In a within‐subjects design, 20 toddlers (age 3–4) discussed a multimedia and a paper elaborated picture with a parent. Results showed that toddlers knew significantly more words (receptively and expressively) after both activities. Moreover, the improvement in receptive vocabulary knowledge was significantly larger with the multimedia elaborated picture compared with the paper‐based picture. In addition, both parent and toddler engaged in a significant higher level of decontextualized language in response to multimedia. The present study shows that multimedia elaborated pictures can support parents at home to engage in a parent–toddler interaction that is richer and supports the development of receptive vocabulary more compared with traditional paper‐based activities. In the future, multimedia‐based home activities for toddlers' vocabulary development, supporting parent–child interaction, can be offered relatively cheap and easily via internet.