Abstract The use of primitive content features of images for classification and retrieval has matured over the past decade. However, human beings often prefer to locate images using words. This article proposes a number of methods to utilize image primitives to support term assignment for image classification. Further, the authors propose to release code for image analysis in a common tool set for other researchers to use. Of particular interest to the authors is the expansion of work by researchers in image indexing to include image content based feature extraction capabilities in their work.

The evaluation of question difficulty is usually considered the domain of Latent Trait Theory. However, these methods require standardized question sets normalized by large populations, rendering them inefficient for use in the numerous areas where questions must be evaluated. A new technique is illustrated that models the question-response cycle well, but without the procedural difficulty of the traditional methods.

The method described permits visual analysis of information retrieval experiment results in classic control and treatment group protocols. It is an additional analysis technique for the evaluation of IR procedures that may be conducted within the rich terrain of human visual acuity, supported by two well-known statistical measures.

Multiple similarity measures for five TREC topic-document sets from the LDC TREC Collection Disk 1 are derived from the full text of documents. Each measure on each set is scaled using SAS MDS under ordinal, interval, and MLE assumptions. The resulting 75 permutations are ploted. It is suggested that cosine-vector and overlap measures for similarity appear to recover optimal data relationships among the documents of the five sets. MLE assumptions appear to be required to model the data adequately.

TREC topic specification statements 1–50 are converted to a similarity matrix, scaled, and plotted. Two close topics and two distant topics are selected from within the topic visual field. Subsequent scaling and visualization of documents associated with the close topics reveals a strong mixing of documents from both topic sets. Scaling and visualization of documents associated with the distant topics reveals a bifurcated distribution of documents from both topic sets. Relevant documents in both cases present near the center of both visualizations. Scaling and visualization of documents by multidimensional scaling using a maximum likelihood estimation method is shown to accurately model token similarity relationships among topic specification statements. The implications of these findings for prior critical arguments regarding IR test collections generally, and TREC specifically, by other scholars is examined.

This article is an unpublished manuscript of a paper presented at the 17th Annual Mid-Year Meeting of the American Society for Information Science at Ann Arbor, Michigan, May 15–18, 1988. It is the first recorded proposal for visual support of indexing. The system itself was built and deployed at NASA in the Johnson Space Center Image Archives from 1989 to 1992.

No abstract available.

Visual documents—motion sequences on film, videotape, and digital recordings—constitute a major source of information for the Space Agency, as well as all other government and private sector entities. This article describes a method for automatically selecting key frames from visual documents. These frames may in turn be used to represent the total image sequence of visual documents in visual libraries, hypermedia systems, and training guides. The performance of the abstracting algorithm reduces 51 minutes of video sequences to 134 frames; a reduction of information in the range of 700:1. © 1993 John Wiley & Sons, Inc.

The relationship between scaling practice and scaling theory remains a controversial problem in Information Retrieval research and experimentation. This article reports a test of a general theory of scaling, i.e., Simple Scalability, applied to the stimulus domain of documents represented as abstracts. The significance of Simple Scalability is that it implies three important properties of scales: transitivity, substitutibility, and independence. The test results indicate that, with some reservations, this theory of scaling is applicable to documents. This finding is further applied to the construction of test collections for Information Retrieval research that could more sensitively measure retrieval system alterations through the use of documents scaled not merely by relevance, but rather, by preference. © 1990 John Wiley & Sons, Inc.