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.     

Color image retrieval using multispectral random field texture model and color content features

This paper describes a color-texture-based image retrieval system for query of an image database to find similar images to a target image. The color-texture information is obtained via modeling with the multispectral simultaneous autoregressive (MSAR) random field model. The general color content characterized by ratios of sample color means is also used. The retrieval process involves segmenting the image into regions of uniform color texture using an unsupervised histogram clustering approach that utilizes the combination of MSAR and color features. The color-texture content, location, area and shape of the segmented regions are used to develop similarity measures describing the closeness of a query image to database images. These attributes are derived from the maximum fitting square and best fitting ellipse to each of the segmented regions. The proposed similarity measure combines all these attributes to rank the closeness of the images. The performance of the system is tested on two databases containing synthetic mosaics of natural textures and natural scenes, respectively.     

SIMPLIcity: semantics-sensitive integrated matching for picturelibraries

We present here SIMPLIcity (semantics-sensitive integrated matching for picture libraries), an image retrieval system, which uses semantics classification methods, a wavelet-based approach for feature extraction, and integrated region matching based upon image segmentation. An image is represented by a set of regions, roughly corresponding to objects, which are characterized by color, texture, shape, and location. The system classifies images into semantic categories. Potentially, the categorization enhances retrieval by permitting semantically-adaptive searching methods and narrowing down the searching range in a database. A measure for the overall similarity between images is developed using a region-matching scheme that integrates properties of all the regions in the images. The application of SIMPLIcity to several databases has demonstrated that our system performs significantly better and faster than existing ones. The system is fairly robust to image alterations     

Multiple-instance content-based image retrieval employing isometric embedded similarity measure

In image-based retrieval, global or local features sufficiently discriminative to summarize the image content are commonly extracted first. Traditional features, such as color, texture, shape or corner, characterizing image content are not reliable in terms of similarity measure. A good match in the feature domain does not necessarily map to image pairs with similar relationship. Applying these features as search keys may retrieve dissimilar false-positive images, or leave similar false-negative ones behind. Moreover, images are inherently ambiguous since they contain a great amount of information that justifies many different facets of interpretation. Using a single image to query a database might employ features that do not match user's expectation and retrieve results with low precision/recall ratios. How to automatically extract reliable image features as a query key that matches user's expectation in a content-based image retrieval (CBIR) system is an important topic.The objective of the present work is to propose a multiple-instance learning image retrieval system by incorporating an isometric embedded similarity measure. Multiple-instance learning is a way of modeling ambiguity in supervised learning given multiple examples. From a small collection of positive and negative example images, semantically relevant concepts can be derived automatically and employed to retrieve images from an image database. Each positive and negative example images are represented by a linear combination of fractal orthonormal basis vectors. The mapping coefficients of an image projected onto each orthonormal basis constitute a feature vector. The Euclidean-distance similarity measure is proved to remain consistent, i.e., isometric embedded, between any image pairs before and after the projection onto orthonormal axes. Not only similar images generate points close to each other in the feature space, but also dissimilar ones produce feature points far apart.The utilization of an isometric-embedded fractal-based technique to extract reliable image features, combined with a multiple-instance learning paradigm to derive relevant concepts, can produce desirable retrieval results that better match user's expectation. In order to demonstrate the feasibility of the proposed approach, two sets of test for querying an image database are performed, namely, the fractal-based feature extraction algorithm vs. three other feature extractors, and single-instance vs. multiple-instance learning. Both the retrieval results, execution time and precision/recall curves show favorably for the proposed multiple-instance fractal-based approach.     

A semantic model for general purpose content-based image retrieval systems

The ‘semantic gap’ is the main challenge in content-based image retrieval. To overcome this challenge, we propose a semantic-based model to retrieve images efficiently with considering user-interested concepts. In this model, an interactive image segmentation algorithm is carried out on the query image to extract the user-interested regions. To recognize the image objects from regions, a neural network classifier is used in this model. In order to have a general-purpose system, no priori assumptions should be made regarding the nature of images in extracting features. So a large number of features should be extracted from all aspect of the image. The high dimensional feature space, not only increases the complexity and required memory, but also may reduce the efficiency and accuracy. Hence, the ant colony optimization algorithm is employed to eliminate irrelevant and redundant features. To find the most similar images to the query image, the similarity between images is measured based on their semantic objects which are defined according to a predefined ontology.     

A scheme of colour image retrieval from databases

This paper presents a scheme of image retrieval from a database using queries prompted by the colour and the shape of the objects present in different scenes. Of the whole scheme of image retrieval, we will focus attention on the modules that allow feature extraction of the component objects from the scenes and the matching of the objects among the different images. The defined scheme enables the indexing of images by measuring the similarity between the integral objects.     

Signature Detection and Matching for Document Image Retrieval

As one of the most pervasive methods of individual identification and document authentication, signatures present convincing evidence and provide an important form of indexing for effective document image processing and retrieval in a broad range of applications. However, detection and segmentation of free-form objects such as signatures from clustered background is currently an open document analysis problem. In this paper, we focus on two fundamental problems in signature-based document image retrieval. First, we propose a novel multiscale approach to jointly detecting and segmenting signatures from document images. Rather than focusing on local features that typically have large variations, our approach captures the structural saliency using a signature production model and computes the dynamic curvature of 2D contour fragments over multiple scales. This detection framework is general and computationally tractable. Second, we treat the problem of signature retrieval in the unconstrained setting of translation, scale, and rotation invariant nonrigid shape matching. We propose two novel measures of shape dissimilarity based on anisotropic scaling and registration residual error and present a supervised learning framework for combining complementary shape information from different dissimilarity metrics using LDA. We quantitatively study state-of-the-art shape representations, shape matching algorithms, measures of dissimilarity, and the use of multiple instances as query in document image retrieval. We further demonstrate our matching techniques in offline signature verification. Extensive experiments using large real-world collections of English and Arabic machine-printed and handwritten documents demonstrate the excellent performance of our approaches.     

基于颜色-空间特征的图像检索

虽然基于颜色直方图特征的图像检索方法简单、高效,但却丢失了颜色的空间分布信息.提出了一种基于颜色-空间特征的图像检索方法.该方法将图像内容看成由若干对象组成的集合,首先利用图像分割得到主要对象,然后根据对象的颜色、位置和形状特征计算图像间内容的相似度,再进行检索.实验结果表明,当图像中有明显的物体时,该方法与颜色直方图相比,能够更加准确和高效地查找出用户所需内容的图像,明显地提高了检索精度.     

A content based image retrieval system for a biological specimen collection

Digital photography and decreasing cost of storing data in digital form has led to an explosion of large digital image repositories. Since the number of images in image databases can be large (millions in some cases) it is important to develop automated tools to search them. In this paper, we present a content based image retrieval system for a database of parasite specimen images. Unlike most content based image retrieval systems, where the database consists of objects that vary widely in shape and size, the objects in our database are fairly uniform. These objects are characterized by flexible body shapes, but with fairly rigid ends. We define such shapes to be FleBoRE (Flexible Body Rigid Extremities) objects, and present a shape model for this class of objects. We have defined similarity functions to compute the degree of likeness between two FleBoRE objects and developed automated methods to extract them from specimen images. The system has been tested with a collection of parasite images from the Harold W. Manter Laboratory for Parasitology. Empirical and expert-based evaluations show that query by shape approach is effective in retrieving specimens of the same class.     

Probability based document clustering and image clustering using content-based image retrieval

Clustering of related or similar objects has long been regarded as a potentially useful contribution of helping users to navigate an information space such as a document collection. Many clustering algorithms and techniques have been developed and implemented but as the sizes of document collections have grown these techniques have not been scaled to large collections because of their computational overhead. To solve this problem, the proposed system concentrates on an interactive text clustering methodology, probability based topic oriented and semi-supervised document clustering. Recently, as web and various documents contain both text and large number of images, the proposed system concentrates on content-based image retrieval (CBIR) for image clustering to give additional effect to the document clustering approach. It suggests two kinds of indexing keys, major colour sets (MCS) and distribution block signature (DBS) to prune away the irrelevant images to given query image. Major colour sets are related with colour information while distribution block signatures are related with spatial information. After successively applying these filters to a large database, only small amount of high potential candidates that are somewhat similar to that of query image are identified. Then, the system uses quad modelling method (QM) to set the initial weight of two-dimensional cells in query image according to each major colour and retrieve more similar images through similarity association function associated with the weights. The proposed system evaluates the system efficiency by implementing and testing the clustering results with Dbscan and K-means clustering algorithms. Experiment shows that the proposed document clustering algorithm performs with an average efficiency of 94.4% for various document categories.     

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.     

Similar-shape retrieval in shape data management

Addresses the problem of similar-shape retrieval, where shapes or images in a shape database that satisfy specified shape-similarity constraints with respect to the query shape or image must be retrieved from the database. In its simplest form, the similar-shape retrieval problem can be stated as, “retrieve or select all shapes or images that are visually similar to the query shape or the query image's shape”. We focus on databases of 2D shapes-or equivalently, databases of images of flat or almost flat objects. (We use the terms “object” and “shape” interchangeably). Two common types of 2D objects are rigid objects, which have a single rigid component called a link, and articulated objects, which have two or more rigid components joined by movable (rotating or sliding) joints. An ideal similar-shape retrieval technique must be general enough to handle images of articulated as well as rigid objects. It must be flexible enough to handle simple query images, which have isolated shapes, and complex query images, which have partially visible, overlapping or touching objects. We discuss the central issues in similar-shape retrieval and explain how these issues are resolved in a shape retrieval scheme called FIBSSR (Feature Index-Based Similar-Shape Retrieval). This new similar-shape retrieval system effectively models real-world applications     

On the cost of searching signature trees

A precise analysis of the retrieval of signature trees is presented. A signature tree is a data structure constructed over a signature file to speed up searching all those signatures, which match a given query signature. The methods used include a detailed study of probabilistic analysis in conjunction with suitable contour integration of complex variabled functions.     

细粒度民族服饰图像检索的全局-局部特征提取方法

民族服饰图像具有不同民族风格的服装款式、配饰和图案,导致民族服饰图像细粒度检索准确率较低.因此,文中提出细粒度民族服饰图像检索的全局-局部特征提取方法.首先,基于自定义的民族服饰语义标注,对输入图像进行区域检测,分别获得前景、款式、图案和配饰图像.然后在全卷积网络结构的基础上构建多分支的全局-局部特征提取模型,对不同区...     

Adaptive image segmentation for region-based object retrieval using generalized Hough transform

Finding an object inside a target image by querying multimedia data is desirable, but remains a challenge. The effectiveness of region-based representation for content-based image retrieval is extensively studied in the literature. One common weakness of region-based approaches is that perform detection using low level visual features within the region and the homogeneous image regions have little correspondence to the semantic objects. Thus, the retrieval results are often far from satisfactory. In addition, the performance is significantly affected by consistency in the segmented regions of the target object from the query and database images. Instead of solving these problems independently, this paper proposes region-based object retrieval using the generalized Hough transform (GHT) and adaptive image segmentation. The proposed approach has two phases. First, a learning phase identifies and stores stable parameters for segmenting each database image. In the retrieval phase, the adaptive image segmentation process is also performed to segment a query image into regions for retrieving visual objects inside database images through the GHT with a modified voting scheme to locate the target visual object under a certain affine transformation. The learned parameters make the segmentation results of query and database images more stable and consistent. Computer simulation results show that the proposed method gives good performance in terms of retrieval accuracy, robustness, and execution speed.     

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