This paper evaluates the accuracy and efficiency of collection-specific texture features for Content-Based Image Retrieval. Independent Component Analysis is used to extract Independent Component Filters (ICF) from an image set. As these ICF are learned from the image set, the hypothesis is that they should provide texture features that are more effective than those extracted using generic filter banks. We describe a method for extracting candidate ICF from an image set, and choosing a representative subset from them. These are then used to extract image features. A simple CBIR system has been developed to evaluate the performance of these features on two standard texture image collections, compared with features extracted using multiple banks of Gabor filters. The results indicate that ICF-based features perform better than Gabor-based features, even when a much smaller number of ICF features is used than Gabor features. The ICF features are thus more accurate, and more efficient.

Content Extraction Signatures (CES) enable selective disclosure of verifiable content, provide privacy for blinded content, and enable the signer to specify the content the document owner is allowed to extract or blind. Combined, these properties give what we call CES functionality. In this paper we describe our work in developing custom transform algorithms to expand the functionality of an XML Signature to include CES functionality in XML Signature Core Validation.We also describe a custom revocation mechanism and our implementation for non-XML content where the custom transforms are dynamically loaded demonstrating that custom signing and verification is not constrained to a 'closed system'. Through the use of dynamic loading we show that a verifier can still verify an XML Signature-compliant signature even though a custom signature was produced.

Benchmarking has always been a crucial problem in content-based image retrieval (CBIR). A key issue is the lack of a common access method to retrieval systems, such as SQL for relational databases. The Multimedia Retrieval Mark-up Language (MRML) solves this problem by standardizing access to CBIR systems (CBIRSs). Other difficult problems are also shortly addressed, such as obtaining relevance judgments and choosing a database for performance comparison. In this article we present a fully automated benchmark for CBIRSs based on MRML, which can be adapted to any image database and almost any kind of relevance judgment. The test evaluates theperformance of positive and negative relevance feedback, which can be generated automatically from the relevance judgments. To illustrate our purpose, a freely available, non-copyright image collection is used to evaluate our CBIRS, Viper. All scripts described here are also freely available for download.

Abstract In this article we address the problem of benchmarking image browsers. Image browsers are systems that help the user in finding an image from scratch, as opposed to query by example (QBE), where an example image is needed. The existence of different search paradigms for image browsers makes it difficult to compare image browsers. Currently, the only admissible way of evaluation is by conducting large‐scale user studies. This makes it difficult to use such an evaluation as a tool for improving browsing systems. As a solution, we propose an automatic image browser benchmark that uses structured text annotation of the image collection for the simulation of the user's needs. We apply such a benchmark on an example system.

Relevance feedback has been shown to be a very effective tool for enhancing retrieval results in text retrieval. It has also been increasingly used in content-based image retrieval and very good results have been obtained. However, too much negative feedback may destroy a query as good features get negative weightings. This paper compares a variety of strategies for positive and negative feedback. The performance evaluation of feedback algorithms is a hard problem. To solve this, we obtain judgments from several users and employ an automated feedback scheme. We then evaluate different techniques using the same judgements. Using automated feedback, the ability of a system to adapt to the user's needs can be measured very effectively. Our study highlights the utility of negative feedback, especially over several feedback steps.

In this paper we employ human judgments of image similarity to improve the organization of an image database. We first derive a statistic, /spl kappa//sub B/ which measures the agreement between two partitionings of an image set. /spl kappa//sub B/ is used to assess agreement both amongst and between human and machine partitionings. This provides a rigorous means of choosing between competing image database organization systems, and of assessing the performance of such systems with respect to human judgments. Human partitionings of an image set are used to define a similarity value based on the frequency with which images are judged to be similar. When this measure is used to partition an image set using a clustering technique, the resultant partitioning agrees better with human partitionings than any of the feature-space-based techniques investigated. Finally, we investigate the use of multilayer perceptrons and a distance learning network to learn a mapping from feature space to this perceptual similarity space. The distance learning network is shown to learn a mapping which results in partitionings in excellent agreement with those produced by human subjects.

This paper describes a two-stage statistical approach supporting content-based search in image databases. The first stage performs correspondence analysis, a factor analysis method transforming image attributes into a reduced-size, uncorrelated factor space. The second stage performs ascendant hierarchical classification, an iterative clustering method which constructs a hierarchical index structure for the images of the database. Experimental results supporting the applicability of both techniques to data sets of heterogeneous images are reported.