Similarity retrieval of iconic image database

The perception of spatial relationships among objects in a picture is one of the important selection criteria to discriminate and retrieve the images in an iconic image database system. The data structure called 2D string, proposed by Chang et al., is adopted to represent symbolic pictures. The 2D string preserves the objects' spatial knowledge embedded in images. Since spatial relationship is a fuzzy concept, the capability of similarity retrieval for the retrieval by subpicture is essential. In this paper, similarity measure based on 2D string longest common subsequence is defined. The algorithm for similarity retrieval is also proposed. Similarity retrieval provides the iconic image database with the distinguishing function different from a conventional database.     

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     

Spatial inference and similarity retrieval of an intelligent image database system based on object's spanning representation

In this paper, we presented a novel image representation method to capture the information about spatial relationships between objects in a picture. Our method is more powerful than all other previous methods in terms of accuracy, flexibility, and capability of discriminating pictures. In addition, our method also provides different degrees of granularity for reasoning about directional relations in both 8- and 16-direction reference frames. In similarity retrieval, our system provides twelve types of similarity measures to support flexible matching between the query picture and the database pictures. By exercising a database containing 3600 pictures, we successfully demonstrated the effectiveness of our image retrieval system. Experiment result showed that 97.8% precision rate can be achieved while maintaining 62.5% recall rate; and 97.9% recall rate can be achieved while maintaining 51.7% precision rate. On an average, 86.1% precision rate and 81.2% recall rate can be achieved simultaneously if the threshold is set to 0.5 or 0.6. This performance is considered to be very good as an information retrieval system.     

An efficient iconic indexing strategy for image rotation and reflection in image databases

Spatial relationships are important issues for similarity-based retrieval in many image database applications. With the popularity of digital cameras and the related image processing software, a sequence of images are often rotated or flipped. That is, those images are transformed in the rotation orientation or the reflection direction. However, many iconic indexing strategies based on symbolic projection are sensitive to rotation or reflection. Therefore, these strategies may miss the qualified images, when the query is issued in the orientation different from the orientation of the database images. To solve this problem, some researchers proposed a function to map the spatial relationship to its transformed one. However, this mapping consists of several conditional statements, which is time-consuming. Thus, in this paper, we propose an efficient iconic indexing strategy, in which we carefully assign a unique bit pattern to each spatial relationship and record the spatial information based on the bit patterns in a matrix. Without generating the rotated or flipped image, we can directly derive the index of the rotated or flipped image from the index of the original one by bit operations and matrix manipulation. In our performance study, we analyze the time complexity of our proposed strategy and show the efficiency of our proposed strategy according to the simulation results. Moreover, we implement a prototype to validate our proposed strategy.     

Graph-based representation for similarity retrieval of symbolic images

Image retrieval from an image database by the image objects and their spatial relationships has emerged as an important research subject in these decades. To retrieve images similar to a given query image, retrieval methods must assess the similarity degree between a database image and the query image by the extracted features with acceptable efficiency and effectiveness. This paper proposes a graph-based model SRG (spatial relation graph) to represent the semantic information of the contained objects and their spatial relationships in an image with no file annotation. In an SRG graph, the image objects are symbolized by the predefined class names as vertices and the spatial relations between object pairs are represented as arcs. The proposed model assesses the similarity degree between two images by calculating the maximum common subgraph of two corresponding SRG’s through intersection, which has quadratic time complexity owing to the characteristics of SRG. Its efficiency remains quadratic regardless of the duplication rate of the object symbols. The extended model SRG_T is also proposed, with the same time complexity, for the applications that need to consider the topological relations among objects. A synthetic symbolic image database and an existing image dataset are used in the conducted experiments to verify the performance of the proposed models. The experimental results show that the proposed models have compatible retrieval quality with remarkable efficiency improvements compared with three well-known methods LCS_Clique, SIM_R, and 2D Be-string, where LCS_Clique utilizes the number of objects in the maximum common subimage as its similarity function, SIM_R uses accumulation-based similarity function of similar object pairs, and 2D Be-string calculates the similarity of 2D patterns by the linear combination of two 1D similarities.     

面向空间应用的数据对象存储与检索系统研究

针对当前标准对象存储系统的数据对象属性存储和检索方面的不足,结合空间应用数据对象特点,进行了对象属性设计,提出了一种基于HBase数据库的属性管理方法。该方法利用HBase数据库对数据对象及其属性进行集中管理,同时采用分段散列索引实现利用对象属性快速检索对象数据。测试结果表明,基于HBase数据库对象管理方法性能优于现有的属性管理方法,具有较高的对象属性检索效率。     

Image database design based on 9D-SPA representation for spatial relations

Spatial relationships between objects are important features for designing a content-based image retrieval system. We propose a new scheme, called 9D-SPA representation, for encoding the spatial relations in an image. With this representation, important functions of intelligent image database systems such as visualization, browsing, spatial reasoning, iconic indexing, and similarity retrieval can be easily achieved. The capability of discriminating images based on 9D-SPA representation is much more powerful than any spatial representation method based on minimum bounding rectangles or centroids of objects. The similarity measures using 9D-SPA representation provide a wide range of fuzzy matching capability in similarity retrieval to meet different user's requirements. Experimental results showed that our system is very effective in terms of recall and precision. In addition, the 9D-SPA representation can be incorporated into a two-level index structure to help reduce the search space of each query processing. The experimental results also demonstrated that, on average, only 0.1254 percent /spl sim/ 1.6829 percent of symbolic pictures (depending on various degrees of similarity) were accessed per query in an image database containing 50,000 symbolic pictures.     

Ontology-based topological representation of remote-sensing images

This article proposes an ontology-based topological representation of remote-sensing images. Semantics, especially related to the topological relationships between the objects represented, are not explicit in remote-sensing images and this fact limits spatial analysis. Our aim is to provide an explicit ontological definition of the topological relations between objects in the image using the Quadtree data structure for spatial indexing. This structure is explicitly defined in an ontology allowing the automatic interpretation of the representations obtained, taking into account the topological relations and increasing the spatial analytical capabilities. This representation has been validated by a case study of semantic retrieval based on the normalized difference vegetation index (NDVI), taking into account the topological relations between NDVI regions in images. In the experiments, we compare the effectiveness of results from eight queries using four traditional supervised image classification algorithms and the proposal representation. The experimental results show the feasibility of the proposal, supporting the concept of the image retrieval process providing a semantic complement to remote-sensing images. The proposed representation contributes to incorporation of semantics into geographical data, especially to remote-sensing images, and it can be used to develop applications in the Geospatial Semantic Web.     

Content-based image retrieval and semantic automatic image annotation based on the weighted average of triangular histograms using support vector machine

In recent years, the rapid growth of multimedia content makes content-based image retrieval (CBIR) a challenging research problem. The content-based attributes of the image are associated with the position of objects and regions within the image. The addition of image content-based attributes to image retrieval enhances its performance. In the last few years, the bag-of-visual-words (BoVW) based image representation model gained attention and significantly improved the efficiency and effectiveness of CBIR. In BoVW-based image representation model, an image is represented as an order-less histogram of visual words by ignoring the spatial attributes. In this paper, we present a novel image representation based on the weighted average of triangular histograms (WATH) of visual words. The proposed approach adds the image spatial contents to the inverted index of the BoVW model, reduces overfitting problem on larger sizes of the dictionary and semantic gap issues between high-level image semantic and low-level image features. The qualitative and quantitative analysis conducted on three image benchmarks demonstrates the effectiveness of the proposed approach based on WATH.     

基于数据隐藏的图像检索系统

本文提出一种新的图像检索系统,采用数据隐藏算法把图像的标注信息和特征信息嵌入压缩域图像中,用于快速检索。由于DCT在图像压缩中得到了广泛的应用,本文分析了基于DCT的JPEG图像格式编码流程,实现了一种基于JPEG压缩的数据隐藏算法。实验结果表明,该算法未引起图像质量明显下降,可嵌入较多数据。     

Signature file as a spatial filter for iconic image database

The problems of efficient data storage and data retrieval are important issues in the design of image database systems. A data structure called a 2-D string, which represents symbolic pictures preserving spatial knowledge, was proposed by Chang et al. It allows a natural way to construct iconic indexes for pictures. We proposed a data structure 2-D B-string to characterize the spatial knowledge embedded in images. It is powerful enough to describe images with partly overlapping or completely overlapping objects without the need of partitioning objects. When there exist a large volume of complex images in the image database, the processing time for image retrieval is tremendous. It is essential to develop efficient access methods for retrieval. In this paper, access methods, to different extents of precision, for retrieval of desired images encoded in 2-D B-strings are proposed. The signature file acting as a spatial filter of image database is based on disjoint coding and superimposed coding techniques. It provides an efficient way to retrieve images in image databases.     

A formal framework for image indexing with triples: Toward a concept-based image retrieval

This paper provides a formal specification for concept-based image retrieval using triples. To effectively manage a vast amount of images, we may need an image retrieval system capable of indexing and searching images based on the characteristics of their content. However, such a content-based image retrieval technique alone may not satisfy user queries if retrieved images turn out to be relevant only when they are conceptually related with the queries. In this paper, we develop an image retrieval mechanism to extract semantics of images based on triples. The semantics can be captured by deriving concepts from its constituent objects and spatial relationships between them. The concepts are basically composite objects formed from the aggregation of the constituents. In our mechanism, all the spatial relationships between objects including the concepts are uniformly represented by triples, which are used for indexing images as well as capturing their semantics. We also develop a query evaluation for supporting the concept-based image retrieval. ©1999 John Wiley & Sons, Inc.     

图像中物体空间关系的表示及在检索中的应用

基于图像中物体之间的空间关系的图像检索往往受困于待处理的图像中物体种类和空间位置难以自动准确地获取。文中基于物体识别算法的输出,提出一种对物体空间关系的三元组表示法,给出基于这种表示方法对图像索引、相似度计算和检索排序的方法及允许用户使用查询词和空间关系表达查询需求的二维输入界面,并实现原型系统。这种表示法具有良好的鲁棒性,可容忍物体识别算法一定程度的误差,将物体识别得到的置信度加入三元组表示法置信度计算和排序算法中,减少物体识别结果误差对检索性能的影响。在原型系统上的实验表明,该系统在实验中对包含物体位置关系的检索给出更准确的结果,在NDCG@m、MAP、F@m上均优于现有系统。     

Statistical correlation analysis in image retrieval

A statistical correlation model for image retrieval is proposed. This model captures the semantic relationships among images in a database from simple statistics of user-provided relevance feedback information. It is applied in the post-processing of image retrieval results such that more semantically related images are returned to the user. The algorithm is easy to implement and can be efficiently integrated into an image retrieval system to help improve the retrieval performance. Preliminary experimental results on a database of 100,000 images show that the proposed model could improve image retrieval performance for both content- and text-based queries.     

Content-based Access in Image Database by Quantitative Relationships

In this paper, we describe a novel technique to perform content-based access in image databases using quantitative spatial relationships. Usually, spatial relation-based indexing methods fail if the metric spatial information contained in the images must be preserved. In order to provide a more robust approach to directional relations indexing with respect to metric differences in images, this paper introduces an improvement of the virtual image index, namely quantitative virtual image, using a quantitative methodology. A scalar quantitative measure is associated with each spatial relation, in order to discriminate among images of the image database having the same objects and spatial relationships, but different degree of similarity if we also consider distance relationships. The measure we introduce does not correspond to any significant increase of complexity with respect to the standard virtual image providing a more precise answer set.     

Browsing museum image collections on a multi-touch table

In this paper we present a multi-touch tabletop system for browsing image databases, conceived for museums and art gallery exhibitions. The system exploits an innovative image browsing paradigm and image retrieval functionalities to perform natural and intuitive user interaction: users can explore the image database by handling digital pictures by means of touch gestures or by the use of a predetermined set of physical objects; once one of these objects is placed on the table, it is automatically recognized and the associated function is triggered. The set of objects and the function associations can be dynamically configured. An innovative feature of our application is that users can interactively create and manipulate image clusters where images are grouped according to their pictorial similarity. This is achieved by placing one or more specific tangible objects on the table surface. The system has been evaluated on a collection of photos organized in groups according to the UNESCO picture categories. The usability tests, performed considering different user categories, show that users consider the application to be attractive and interesting.     

A knowledge-based approach for retrieving images by content

A knowledge based approach is introduced for retrieving images by content. It supports the answering of conceptual image queries involving similar-to predicates, spatial semantic operators, and references to conceptual terms. Interested objects in the images are represented by contours segmented from images. Image content such as shapes and spatial relationships are derived from object contours according to domain specific image knowledge. A three layered model is proposed for integrating image representations, extracted image features, and image semantics. With such a model, images can be retrieved based on the features and content specified in the queries. The knowledge based query processing is based on a query relaxation technique. The image features are classified by an automatic clustering algorithm and represented by Type Abstraction Hierarchies (TAHs) for knowledge based query processing. Since the features selected for TAH generation are based on context and user profile, and the TAHs can be generated automatically by a clustering algorithm from the feature database, our proposed image retrieval approach is scalable and context sensitive. The performance of the proposed knowledge based query processing is also discussed     

Constraint Based Region Matching for Image Retrieval

Objects and their spatial relationships are important features for human visual perception. In most existing content-based image retrieval systems, however, only global features extracted from the whole image are used. While they are easy to implement, they have limited power to model semantic-level objects and spatial relationship. To overcome this difficulty, this paper proposes a constraint-based region matching approach to image retrieval. Unlike existing region-based approaches where either individual regions are used or only first-order constraints are modeled, the proposed approach formulates the problem in a probabilistic framework and simultaneously models both first-order region properties and second-order spatial relationships for all the regions in the image. Specifically, in this paper we present a complete system that includes image segmentation, local feature extraction, first- and second-order constraints, and probabilistic regionweight estimation. Extensive experiments have been carried out on a large heterogeneous image collection with 17,000 images. The proposed approach achieves significantly better performance than the state-of-the-art approaches.