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Bollmann-Sdorra, P.; Raghavan, V.V.: On the necessity of term dependence in a query space for weighted retrieval (1998) 0.05
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Abstract

In recent years, in the context of the vector space model, the view, held by many researchers, that documents, queries, terms, etc., are all elements of a common space has been challenged (Bollmann-Sdorra & Raghavan, 1993). In particular, it was noted that term independence has to be investigated in the context of user preferences and it was shown, through counterexamples, that term independence can hold in the document space, but not in the query space and vice versa. In this article, we continue the investigation of query and document spaces with respect to the property of term independence. We prove, under realistic assumptions, that requiring term independence to hold in the query space is inconsistent with the goal of achieving better performance by means of weighted retrieval. The result that term independence in the query space is undesirable is obtained without making any assumption about wjether or not the property of term independence holds in the document space. The results of this article reinforce our position that the properties of document and query spaces must be investigated separately, since the document and query spaces do not necessarily have the same properties
Bollmann-Sdorra, P.; Raghavan, V.V.: On the delusiveness of adopting a common space for modelling IR objects : are queries documents? (1993) 0.03
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Abstract

Many authors, who adopt the vector space model, take the view that documents, terms, queries, etc., are all elements within the same (conceptual) space. This view seems to be a natural one, given that documents and queries have the same vector notation. We show, however, that the structure of the query space can be very different from that of the document space. To this end, concepts like preference, similarity, term independence, and linearity, both in the document space and in the query space, are discussed. Our conclusion is that a more realistic and complete view of IR is obtained if we do not consider documents and queries to be elements of the same space. This conclusion implies that certain restrictions usually applied in the design of an IR system are obviated. For example, the retrieval function need not be interpreted as a similarity measure
Gudivada, V.N.; Raghavan, V.V.: Modeling and retrieving images by content (1997) 0.03
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Abstract

Provides a taxonomy for approaches to image retrieval and describes their chrarcteristics and limitations. Examines a number of image database applications to discover their retrieval requirements and to structure the requirements from a domain-independent perspective. Provides a taxonomy for image attributes and proposes a number of generic query operators. These operators a re adequate to realise content-based image retrieval system (CBIR) in anumber of diverse applications. Proposes a novel system architecture for CBIR that supports the generic query operators. A partial prototype has been implemented. Demonstrates the versatility and effectiveness of the architecture by configuring the prototype implementation for 2 image retrieval applications: realtors information system and face retrieval system. The 1st application is for real estate marketing and the other is for law enforcement and criminal investigation
Bhatia, S.K.; Deogun, J.S.; Raghavan, V.V.: Conceptual query formulation and retrieval (1995) 0.03
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Abstract

In this paper, we advance a technique to develop a user profile for information retrieval through knowledge acquisition techniques. The profile bridges the discrepancy between user-expressed keywords and system-recognizable index terms. The approach presented in this paper is based on the application of personal construct theory to determine a user's vocabulary and his/her view of different documents in a training set. The elicited knowledge is used to develop a model for each phrase/concept given by the user by employing machine learning techniques. Our model correlates the concepts in a user's vocabulary to the index terms present in the documents in the training set. Computation of dependence between the user phrases also contributes in the development of the user profile and in creating a classification of documents. The resulting system is capable of automatically identifying the user concepts and query translation to index terms computed by the conventional indexing process. The system is evaluated by using the standard measures of precision and recall by comparing its performance against the performance of the smart system for different queries.
Raghavan, V.V.; Jung, G.S.; Bollmann, P.: ¬A critical investigation of recall and precision as measures of retrieval system performance (1989) 0.03
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Abstract

Recall and precision are often used to evaluate the effectiveness of information retrieval systems. However, when the retrieval results are weakly ordered, in the sense that several documents have an identical retrieval status value with respect to a query, some prohabilistic notion of precision has to be introduced. Provides a comparative analysis of methods available for defining precision in a prohabilistic sense and to promote a better understanding of the various issues involved in retrieval performance evaluation.
Gudivada, V.N.; Raghavan, V.V.: Design and evaluation of algorithms for image retrieval by spatial similarity (1995) 0.03
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Abstract

A major class of the requests from image database users involves similarity based retrieval for finding images spatially similar to a query image. Proposes an algorithm for computing the spatial similarity between 2 symbolic images. A symbolic image is a logical representation of the original image where the image objects are uniquely labelled with symbolic names. The algorithm can deal with translation, scale and rotational variance in images. Compares the characteristics of the proposed algorithm with those previously available by using a testbed of images. The proposed algorithm is more efficient and provides a rank ordering consistently matching with an expert

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