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.     

An intelligent image database system

A prototype intelligent image database system (IIDS) that is based on a novel pictorial data structure is presented. This prototype system supports spatial reasoning, flexible image information retrieval, visualization, and traditional image database operations. A pictorial data structure, based on 2-D strings, provides an efficient means for iconic indexing in image database systems and spatial reasoning. The modular design of IIDS facilitates its implementation. Further extensions of the prototype system are discussed     

Visual image retrieval by elastic matching of user sketches

Effective image retrieval by content from database requires that visual image properties are used instead of textual labels to properly index and recover pictorial data. Retrieval by shape similarity, given a user-sketched template is particularly challenging, owing to the difficulty to derive a similarity measure that closely conforms to the common perception of similarity by humans. In this paper, we present a technique which is based on elastic matching of sketched templates over the shapes in the images to evaluate similarity ranks. The degree of matching achieved and the elastic deformation energy spent by the sketch to achieve such a match are used to derive a measure of similarity between the sketch and the images in the database and to rank images to be displayed. The elastic matching is integrated with arrangements to provide scale invariance and take into account spatial relationships between objects in multi-object queries. Examples from a prototype system are expounded with considerations about the effectiveness of the approach and comparative performance analysis     

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.     

基于内容的标准烟叶图像数据库检索

基于内容的检索是当前多媒体数据库发展的一个重要研究领域。在图像数据库中,基于内容的图像检索技术一般采用颜色直方图为特征。该文提出了把图像的形状特征、颜色特征和纹理特征结合起来的方法进行基于内容的图像检索。通过对人的视觉检索过程的研究,提出把数据库中的图像依次分别按形状特征、颜色特征和纹理特征分级聚类的方法,既符合人的视觉特点又大大提高了检索效率。     

Image database indexing using JPEG coefficients

Image database indexing is used for efficient retrieval of images in response to a query expressed as an example image. The query image is processed to extract information that is matched against the index to provide pointers to similar images. We present a technique that facilitates content similarity-based retrieval of jpeg-compressed images without first having to uncompress them. The technique is based on an index developed from a subset of jpeg coefficients and a similarity measure to determine the difference between the query image and the images in the database. This method offers substantial efficiency as images are processed in compressed format, information that was derived during the original compression of the images is reused, and extensive early pruning is possible. Initial experiments with the index have provided encouraging results. The system outputs a set of ranked images in the database with respect to the query using the similarity measure, and can be limited to output a specified number of matched images by changing the threshold match.     

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.     

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.     

Filter Image Browsing: Interactive Image Retrieval by Using Database Overviews

Human-computer interaction is a decisive factor in effective content-based access to large image repositories. In current image retrieval systems the user refines his query by selecting example images from a relevance ranking. Since the top ranked images are all similar, user feedback often results in rearrangement of the presented images only.For better incorporation of user interaction in the retrieval process, we have developed the Filter Image Browsing method. It also uses feedback through image selection. However, it is based on differences between images rather than similarities. Filter Image Browsing presents overviews of relevant parts of the database to users. Through interaction users then zoom in on parts of the image collection. By repeatedly limiting the information space, the user quickly ends up with a small amount of relevant images. The method can easily be extended for the retrieval of multimedia objects.For evaluation of the Filter Image Browsing retrieval concept, a user simulation is applied to a pictorial database containing 10,000 images acquired from the World Wide Web by a search robot. The simulation incorporates uncertainty in the definition of the information need by users. Results show Filter Image Browsing outperforms plain interactive similarity ranking in required effort from the user. Also, the method produces predictable results for retrieval sessions, so that the user quickly knows if a successful session is possible at all. Furthermore, the simulations show the overview techniques are suited for applications such as hand-held devices where screen space is limited.     

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.     

Utilizing similarity relationships among existing data for high accuracy processing of content-based image retrieval

Retrieving similar images based on its visual content is an important yet difficult problem. We propose in this paper a new method to improve the accuracy of content-based image retrieval systems. Typically, given a query image, existing retrieval methods return a ranked list based on the similarity scores between the query and individual images in the database. Our method goes further by relying on an analysis of the underlying connections among individual images in the database to improve this list. Initially, we consider each image in the database as a query and use an existing baseline method to search for its likely similar images. Then, the database is modeled as a graph where images are nodes and connections among possibly similar images are edges. Next, we introduce an algorithm to split this graph into stronger subgraphs, based on our notion of graph’s strength, so that images in each subgraph are expected to be truly similar to each other. We create for each subgraph a structure called integrated image which contains the visual features of all images in the subgraph. At query time, we compute the similarity scores not only between the query and individual database images but also between the query and the integrated images. The final similarity score of a database image is computed based on both its individual score and the score of the integrated image that it belongs to. This leads effectively to a re-ranking of the retrieved images. We evaluate our method on a common image retrieval benchmark and demonstrate a significant improvement over the traditional bag-of-words retrieval model.     

Expert system for color image retrieval

Recently, as Web and various databases contain a large number of images, content-based image retrieval (CBIR) applications are greatly needed. This paper proposes a new image retrieval system using color-spatial information from those applications.First, this paper suggests two kinds of indexing keys to prune away irrelevant images to a given query image: major colors' set (MCS) signature related with color information and distribution block signature (DBS) related with spatial information. After successively applying these filters to a large database, we get only small amount of high potential candidates that are somewhat similar to a query image. Then we make use of the quad modeling (QM) method to set the initial weights of two-dimensional cell in a query image according to each major color. Finally, we retrieve more similar images from the database by comparing a query image with candidate images through a similarity measuring function associated with the weights. In that procedure, we use a new relevance feedback mechanism. This feedback enhances the retrieval effectiveness by dynamically modulating the weights of color-spatial information. Experiments show that the proposed system is not only efficient but also effective.     

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     

基于颜色连通区域多特征融合的图像检索

颜色一致向量方法容易丢失图像内容的空间信息,针对该问题,提出一种新的图像检索方法。引入狭长度和标准差特征,设计改进的图像分块策略,给出离心距概念和距离比较公式。提取颜色连通区域的大小、狭长度和颜色特征,以及图像的像素个数、标准差和离心距特征,计算图像间内容的相似度。在Corel图像库上的实验结果表明,该方法能有效利用图像的空间分布信息,检索精度较高。     

一种基于对象空间关系的图像检索模型

介绍一种基于空间相似的图像数据库检索模型，提出了空间相似度计算的算法，对对象的空间方位、拓扑关系、数量和空间关系局部高度相似等因素都作了全面考虑，详细阐述空间相似度与局部相似对象簇的求解过程，建立的模型不仅能识别经平移、缩放和旋转交换的图像，而且能识别图像的对称变换，通过实验验证，表明检索模型的图像搜索效率高。     

Picture similarity retrieval using the 2D projection intervalrepresentation

Spatial relationships are important ingredients for expressing constraints in retrieval systems for pictorial or multimedia databases. We have proposed a unified representation for spatial relationships, 2D Projection Interval Relationships (2D-PIR), that integrates both directional and topological relationships. We develop techniques for similarity retrieval based on the 2D-PIR representation, including a method for dealing with rotated and reflected images     

基于分形编码拓扑特性的图像检索

为了更有效、更准确地进行图像检索，提出了一种利用分形编码这项重要的拓扑特性来处理图像索引的新方法，即将图像经分形编码，首先得到每张图像的迭代函数，然后将其伴随图像存人数据库中，成为该图像的索引文件最后对数据库进行搜索时，则通过对此索引文件的比对来找出与查询图像相似的图像。反观使用其他方法建立的图像索引数据库，则无法证明其建立的索引文件具有上述特质。实验显示，图像经过分形编码所表现出的几何性质以及独特的有效性和鲁棒性，证明该方法是一个更有效率、准确度高的检索方法。     

Pictorial Query Specification for Browsing Through Spatially Referenced Image Databases

A pictorial query specification technique that enables the formulation of complex pictorial queries for browsing through a collection of spatially referenced images is presented. It is distinguished from most other methods by the fact that in these methods the query image specifies a target database image in its entirety whereas in our approach the query image specifies the combination of objects that the target database image should contain rather than being treated as a whole image. The query objects are represented by shape features although other features such as color, texture or wavelets could also be used. Using our technique, it is possible to specify which particular objects should appear in the target images as well as how many occurrences of each object are required. Moreover, it is possible to specify the minimum required certainty of matching between query-image objects and database-image objects, as well as to impose spatial constraints that specify bounds on the distance between objects and the relative direction between them. These spatial constraints can also be used to specify other topological relations such as enclosure, intersection, overlap, etc. Each pictorial query is composed of one or more query images. Each query image is constructed by selecting the required query objects and positioning them according to the desired spatial configuration. Boolean combinations of two or more query images are also possible by use of AND and OR operators. A query image may be negated in order to specify conditions that should not be satisfied by the database images that are retrieved successfully. In addition, a capability is provided to specify whether the same instance of an object is to be used when it appears in more than one of the query images that make up the pictorial query, or whether two different instances are allowed. Several example queries are given that demonstrate the expressive power of this query specification method. An algorithm for retrieving all database images that conform to a given pictorial query specification is presented. The user interface for using this pictorial query specification method to browse the results in a map image database application is described and illustrated via screen shots.     

融合整体与分块颜色分布的图像检索方法

针对传统的颜色直方图无法反映色彩分布的空间信息这一缺陷,提出了一种以同一颜色构成的最大连通区域及其边缘颜色粗糙度的图像检索方法,并在此基础上将图像间的相似度定义为整体相似度与以图像分块为基础的局部相似度的加权和,以反映人们对图像相似度的判定过程。实验表明,该方法获得了图像颜色的空间分布,并可以根据图像的颜色空间分布是否集中来调整整体相似度的权值,以提高检索性能,同时弥补了颜色直方图无法反映颜色空间分布的不足,提高了检索的准确性。     

基于空间上下文加权词汇树的图像检索方法

基于词汇树的词袋模型(Bag-of-Words)表示算法是目前图像检索领域中的主流算法.针对传统词汇树方法中空间上下文信息缺失的问题,提出一种基于空间上下文加权词汇树的图像检索方法.该方法在词汇树框架下,首先生成SIFT点的空间上下文信息描述.然后利用SIFT点间的空间上下文相似度对SIFT间的匹配得分进行加权,得到图像间的相似度.最后,通过相似度排序完成图像检索.实验结果表明,该方法能够大幅度提高图像检索的性能,同时,对大规模图像库有较好的适用性.