Texture-based pattern retrieval from image databases

Recent technological advances have made it possible to process and store large amounts of image data. Perhaps the most impressive example is the accumulation of image data in scientific applications such as medical or satellite imagery. However, in order to realize their full potential, tools for efficient extraction of information and for intelligent searches in image databases need to be developed. This paper describes a new approach to image data retrieval which allows queries to be composed of local intensity patterns. The intensity pattern is converted into a feature representation of reduced dimensionality which can be used for searching similar-looking patterns in the database. This representation is obtained by filtering the pattern with a bank of scale and orientation selective filters modeled using Gabor functions. Experimental results are presented which illustrate that the proposed representation preserves the perceptual similarities, and provides a powerful tool for content-based image retrieval.     

Density-based retrieval from high-similarity image databases

Many image classification problems can fruitfully be thought of as image retrieval in a “high similarity image database” (HSID) characterized by being tuned towards a specific application and having a high degree of visual similarity between entries that should be distinguished. We introduce a method for HSID retrieval using a similarity measure based on a linear combination of Jeffreys-Matusita distances between distributions of local (pixelwise) features estimated from a set of automatically and consistently defined image regions. The weight coefficients are estimated based on optimal retrieval performance. Experimental results on the difficult task of visually identifying clones of fungal colonies grown in a petri dish and categorization of pelts show a high retrieval accuracy of the method when combined with standardized sample preparation and image acquisition.     

Geometry-based image retrieval in binary image databases

In this paper, a geometry-based image retrieval system is developed for multi-object images. We model both shape and topology of image objects using a structured representation called curvature tree (CT). The hierarchy of the CT reflects the inclusion relationships between the image objects. To facilitate shape-based matching, triangle-area representation (TAR) of each object is stored at the corresponding node in the CT. The similarity between two multi-object images is measured based on the maximum similarity subtree isomorphism (MSSI) between their CTs. For this purpose, we adopt a recursive algorithm to solve the MSSI problem and a very effective dynamic programming algorithm to measure the similarity between the attributed nodes. Our matching scheme agrees with many recent findings in psychology about the human perception of multi-object images. Experiments on a database of 13500 real and synthesized medical images and the MPEG-7 CE-1 database of 1400 shape images have shown the effectiveness of the proposed method.     

Information retrieval in document image databases

With the rising popularity and importance of document images as an information source, information retrieval in document image databases has become a growing and challenging problem. In this paper, we propose an approach with the capability of matching partial word images to address two issues in document image retrieval: word spotting and similarity measurement between documents. First, each word image is represented by a primitive string. Then, an inexact string matching technique is utilized to measure the similarity between the two primitive strings generated from two word images. Based on the similarity, we can estimate how a word image is relevant to the other and, thereby, decide whether one is a portion of the other. To deal with various character fonts, we use a primitive string which is tolerant to serif and font differences to represent a word image. Using this technique of inexact string matching, our method is able to successfully handle the problem of heavily touching characters. Experimental results on a variety of document image databases confirm the feasibility, validity, and efficiency of our proposed approach in document image retrieval.     

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     

A unified log-based relevance feedback scheme for image retrieval

Relevance feedback has emerged as a powerful tool to boost the retrieval performance in content-based image retrieval (CBIR). In the past, most research efforts in this field have focused on designing effective algorithms for traditional relevance feedback. Given that a CBIR system can collect and store users' relevance feedback information in a history log, an image retrieval system should be able to take advantage of the log data of users' feedback to enhance its retrieval performance. In this paper, we propose a unified framework for log-based relevance feedback that integrates the log of feedback data into the traditional relevance feedback schemes to learn effectively the correlation between low-level image features and high-level concepts. Given the error-prone nature of log data, we present a novel learning technique, named soft label support vector machine, to tackle the noisy data problem. Extensive experiments are designed and conducted to evaluate the proposed algorithms based on the COREL image data set. The promising experimental results validate the effectiveness of our log-based relevance feedback scheme empirically.     

Content-based retrieval from trademark databases

Since the number of registered trademarks is increasing rapidly, the job of identifying infringement of similar trademarks by human inspection becomes laborious and time-consuming. To deal with the problem, we propose an automatic content-based trademark retrieval method. The proposed method automatically selects appropriate features based on feature selection principles to discriminate trademarks. The database trademarks are softly clustered into classes using a fuzzy approach to increase the retrieval speed. The user can submit a query through trademark examples to get a list of database trademarks ordered by similarity ranks. The query results can be iteratively refined by the feedback presented by the user until the trademarks of interest are retrieved. Experiments are conducted on a trademark database containing 1000 images and the retrieval results are very encouraging.     

A multichannel storage arrangement scheme of pictorial databases for concurrent spatial match retrieval

When touching the applications related to pictorial databases, then the first problem that should be faced is the large amount of pictorial data (pictures) to be retrieved. Therefore, in this paper, a multichannel storage arrangement scheme is proposed to allocate the pictures into a multichannel storage system such that the average response time for all possible conjunctions of simple spatial match queries is minimized.     

Numerical scheme for efficient colour image denoising

In this paper, we are interested by the enhancement of colour images, where we present a numerical scheme to implement a non-linear diffusion filter. This scheme is developed to denoise colour images corrupted by additive noise. The method is based on harmonic averaging that takes into account correlation between all colour components of the image by using a common gradient magnitude which can be computed by using the Di Zenzo idea, and a marginal gradient magnitude in order to conserve the self behaviour of each colour component. The proposed scheme is an efficient tool at image selective smoothing in presence of strong noise. This analysis shows that our method performs better than some related works, and particularly in avoiding colour artifacts.     

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.     

Exploiting the JPEG compression scheme for image retrieval

We address the problem of retrieving images from a large database using an image as a query. The method is specifically aimed at databases that store images in JPEG format, and works in the compressed domain to create index keys. A key is generated for each image in the database and is matched with the key generated for the query image. The keys are independent of the size of the image. Images that have similar keys are assumed to be similar, but there is no semantic meaning to the similarity     

Application of planar shape comparison to object retrieval in image databases

A similarity measure for silhouettes of 2D objects is presented, and its properties are analyzed with respect to retrieval of similar objects in image databases. To reduce influence of digitization noise as well as segmentation errors the shapes are simplified by a new process of digital curve evolution. To compute our similarity measure, we first establish the best possible correspondence of visual parts (without explicitly computing the visual parts). Then the similarity between corresponding parts is computed and summed. Experimental results show that our shape matching procedure gives an intuitive shape correspondence and is stable with respect to noise distortions.     

Combining spatial and colour information for content based image retrieval

Colour is one of the most important features in content based image retrieval. However, colour is rarely used as a feature that codes local spatial information, except for colour texture. This paper presents an approach to represent spatial colour distributions using local principal component analysis (PCA). The representation is based on image windows which are selected by two complementary data driven attentive mechanisms: a symmetry based saliency map and an edge and corner detector. The eigenvectors obtained from local PCA of the selected windows form colour patterns that capture both low and high spatial frequencies, so they are well suited for shape as well as texture representation. Projections of the windows selected from the image database to the local PCs serve as a compact representation for the search database. Queries are formulated by specifying windows within query images. System feedback makes both the search process and the results comprehensible for the user.     

Indexing chromatic and achromatic patterns for content-based colour image retrieval

In this paper, we present a method to represent achromatic and chromatic image signals independently for content-based image indexing and retrieval for image database applications. Starting from an opponent colour representation, human colour vision theories and modern digital signal processing technologies are applied to develop a compact and computationally efficient visual appearance model for coloured image patterns. We use the model to compute the statistics of achromatic and chromatic spatial patterns of colour images for indexing and content-based retrieval. Two types of colour images databases, one colour texture database and another photography colour image database are used to evaluate the performance of the developed method in content-based image indexing and retrieval. Experimental results are presented to show that the new method is superior or competitive to state-of-the-art content-based image indexing and retrieval techniques.     

A new watermarking scheme for colour image using QR decomposition and ternary coding

At present, the binary images are often used as the original watermark images of many watermarking methods, but partial methods cannot be easily extended to colour image watermarking methods. For resolving this problem, we propose a new watermarking method using ternary coding and QR decomposition for colour image. In the procedure of embedding watermark, the colour image watermark is coded to ternary information; the colour host image is also separated into image blocks of sized 3?×?3, and these image blocks are further decomposed via QR decomposition; then, one ternary watermark is embedded into one orthogonal matrix Q of QR decomposition by the proposed rules. In the procedure of extracting watermark, the proposed method uses the blind-manner to extract the embedded ternary information. The novelty of this scheme lies in the proposed ternary coding for watermark image, which can improve the imperceptibility, embedded watermark capacity and real-time feature of the watermarking scheme. The results of simulation show the presented technique is better than other compared schemes with respect to imperceptibility, embedded watermark capacity and real-time feature under the similar robustness.

     

Inexact graph matching based on kernels for object retrieval in image databases

In the framework of online object retrieval with learning, we address the problem of graph matching using kernel functions. An image is represented by a graph of regions where the edges represent the spatial relationships. Kernels on graphs are built from kernel on walks in the graph. This paper firstly proposes new kernels on graphs and on walks, which are very efficient for graphs of regions. Secondly we propose fast solutions for exact or approximate computation of these kernels. Thirdly we show results for the retrieval of images containing a specific object with the help of very few examples and counter-examples in the framework of an active retrieval scheme.     

A generic scheme for color image retrieval based on the multivariate Wald-Wolfowitz test

In this study, a conceptually simple, yet flexible and extendable strategy to contrast two different color images is introduced. The proposed approach is based on the multivariate Wald-Wolfowitz test, a nonparametric test that assesses the commonality between two different sets of multivariate observations. It provides an aggregate gauge of the match between color images, taking into consideration all the (selected) low-level characteristics, while alleviating correspondence issues. We show that a powerful measure of similarity between two color images can emerge from the statistical comparison of their representations in a properly formed feature space. For the sake of simplicity, the RGB-space is selected as the feature space, while we are experimenting with different ways to represent the images within this space. By altering the feature-extraction implementation, complementary ways to portray the image content appear. The reported results, from the application on a diverse collection of images, clearly demonstrate the effectiveness of our method, its superiority over previous methods, and suggest that even further improvements can be achieved along the same line of research. It is not only the unifying character that makes our strategy appealing, but also the fact that the retrieval performance does not increase continuously with the amount of details in the image representation. The latter sets an upper limit to the computational demands and reminds of performance plateaus reached by novel approaches in information retrieval.