RISE: A Robust Image Search Engine

In this article we address the problem of organizing images for effective and efficient retrieval in large image database systems. Specifically, we describe the design and architecture of RISE, a Robust Image Search Engine. RISE is designed to build and search an image repository, with an interface that allows for the query and maintenance of the database over the Internet using any browser. RISE is built on the foundation of a CBIR (Content Based Image Retrieval) system and computes the similarity of images using their color signatures. The signature of an image in the database is computed by systematically dividing the image into a set of small blocks of pixels and then computing the average color of each block. This is based on the Discrete Cosine Transform (DCT) that forms the basis for popular JPEG image file format. The average color in each pixel block forms the characters of our image description. Organizing these pixel blocks into a tree structure allows us to create the words or tokens for the image. Thus the tokens represent the spatial distribution of the color in the image. The tokens for each image in the database are first computed and stored in a relational database as their signatures. Using a commercial relational database system (RDBMS) to store and query signatures of images improves the efficiency of the system. A query image provided by a user is first parsed to build the tokens which are then compared with the tokens for images in the database. During the query process, tokenization improves the efficiency by quantifying the degree of match between the query image and images in the database. The content similarity is measured by computing normalized Euclidean distance between corresponding tokens in query and stored images where correspondence is defined by the relative location of those tokens. The location of pixel blocks is maintained by using a quad tree structure that also improves performance by early pruning of search space. The distance is computed in perceptual color space, specifically L * a * b * and at different levels of detail. The perceptual color space allows RISE to ignore small variations in color while different levels of detail allow it to select a set of images for further exploration, or discard a set altogether. RISE only compares the precomputed color signature images that are stored in an RDBMS. It is very efficient since there is no need to extract complete information for every image. RISE is implemented using object-oriented design techniques and is deployed as a web browser-based search engine. RISE has a GUI (Graphical User Interface) front-end and a Java servlet in the back-end that searches the images stored in the database and returns the results to the web browser. RISE enhances the performance of image operations of the system by using JAI (Java Advance Imaging) tools, which obviates the dependence on a single image file format. In addition, the use of RDBMS and Java also facilitates the portability of 1 2 Goswami, Bhatia, Samal the system

Stitching algorithms for biological specimen images

Abstract: In this paper, we address the problem of combining multiple overlapping image sections of biological specimens to obtain a single image containing the entire specimen. This is useful in the digitisation of a large number of biological specimens stored in museum collections and laboratories. In the case of many large specimens, it means that the specimen must be captured in overlapping sections instead of a single image. In this research, we have compared the performance of several known algorithms for this problem. In addition, we have developed several new algorithms based on matching the geometry (width, slope, and curvature) of the specimens at the boundaries. Finally, we compare the performance of a bagging approach that combines the results from multiple stitching algorithms. Our detailed evaluation shows that brightness-based and curvature-based approaches produce the best matches for the images in this domain

Knowledge-based information retrieval and classification

A user of an information retrieval system formulates a query to express his/her information requirements. The query formulation is a difficult process because of the discrepancies between the vocabulary of the user and that of the system. For the system to perform effective retrieval, the query should be in terms of keywords in the system vocabulary. Past efforts for the solution to the problem of query expression have concentrated on relevance feedback, thesaurus construction, and classification using the matching of keywords extracted from the documents in the collection. In this dissertation, an alternative view is proposed to improve the query formulation and classification process. The proposed approach is based on the application of knowledge acquisition techniques to determine a user\u27s vocabulary and his/her view of different documents in a training set. A representation is then developed for each phrase/concept given by the user in terms of keywords extracted by the system from those documents using machine learning techniques. The query given by the user in his/her own vocabulary can then be easily translated into the system vocabulary. Computation of relationships between the phrases given by the user also helps in developing a user profile and creating a classification of documents. The resulting system is capable of automatically identifying the phrases in a user query and correlating them to the keywords computed by the system through the conventional indexing process. In addition, keywords extracted from an incoming document are compared with the representation of various clusters to identify the most appropriate cluster for the document. The application of the developed techniques to message routing and message understanding is also investigated. The system is evaluated by using the standard performance measures of precision and recall by comparing its performance against the performance of the scSMART system for individual queries. The classification results are shown to satisfy the performance criterion for satisfactory classification as published in the literature

Creating large isotropic textures using image quilting

Image quilting is a texture synthesis technique to create a large texture by wrapping around patches of a small texture in a way that the repetition of small texture is not noticeable. The basic algorithm is to randomly select small patches in a given texture. These patches are then positioned in a large texture to be synthesized and blended across boundaries to remove the appearance of boundaries across patches. The algorithm is useful to create large isotropic textures from small isotropic textures. We have extended the algorithm to create large isotropic textures from a given anisotropic texture by using only the desired areas in the synthesized texture

Hierarchical Clustering for Image Databases

The organization of an image database is one of the important issues in efficient storage and retrieval of images. Most of the existing image databases are based on flat structures, with the possibility of an index into the database that can help in narrowing down the images to be searched. In this paper, I’ll present a technique to create a hierarchical data structure based on the clustering approach such that a user can select or discard a number of images for subsequent operations. The presented technique is based on application of wavelet analysis to scale the images in hierarchy, and can take advantage of the structure of compressed images in the JPEG 2000 standard. 1

Experience Summary

• Extensive experience in design and implementation of algorithms for image processing, computer graphics, image databases, data clustering, and information retrieval. • Over 12 years ’ experience in real-time software development projects. • In-depth experience in Unix (Solaris and Linux) and C/C++ development (mostly Gnu environments); some experience with Linux kernel programming • Experienced in programming Linux cluster using MPI; familiar with Grid computing • Over 20 years ’ experience in teaching various Computer Science courses at both graduate and undergraduate levels, at universities as well as in industry. • Extensive project management experience for research, academic, and industrial projects. • Knowledge of Windows and network programming. Employment Summary (Industry