In addition to the frequency of terms in a document collection, the distribution of terms plays an important role in determining the relevance of documents. In this paper, a new approach for representing term positions in documents is presented. The approach allows an efficient evaluation of term-positional information at query evaluation time. Three applications are investigated: a function-based ranking optimization representing a user-defined document region, a query expansion technique based on overlapping the term distributions in the top-ranked documents, and cluster analysis of terms in documents. Experimental results demonstrate the effectiveness of the proposed approach.
One of the first approaches using abstract representations of term distributions in documents is Fourier Domain Scoring (FDS), proposed by @cite_8 . FDS performs a separate magnitude and phase analysis of term position signals to produce an optimized ranking. It creates an index based on page segmentation, storing term frequency and approximated positions in the document. FDS processes the indexed data using the to perform the corresponding spectral analysis.
@cite_8
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In addition to the frequency of terms in a document collection, the distribution of terms plays an important role in determining the relevance of documents for a given search query. In this paper, term distribution analysis using Fourier series expansion as a novel approach for calculating an abstract representation of term positions in a document corpus is introduced. Based on this approach, two methods for improving the evaluation of document relevance are proposed: (a) a function-based ranking optimization representing a user defined document region, and (b) a query expansion technique based on overlapping the term distributions in the top-ranked documents. Experimental results demonstrate the effectiveness of the proposed approach in providing new possibilities for optimizing the retrieval process. Shape is one of the primary low level image features in Content Based Image Retrieval (CBIR). Many shape representations and retrieval methods exist. However, most of those methods either do not well capture shape features or are difficult to do normalization (making matching difficult). Among them, methods based Fourier descriptors (FDs) achieve both good representation (perceptually meaningful) and easy normalization. Besides, FDs are easy to derive and compact in terms of representation. Design of FDs focuses on how to derive Fourier invariants from Fourier coefficients and how to obtain Fourier coefficients from shape signatures. Different Fourier invariants and shape signatures have been exploited to derive FDs. In this paper, we study different FDs and build a Java retrieval framework to compare shape retrieval performance using different FDs in terms of computation complexity, robustness, convergence speed and retrieval performance. The retrieval performance of the different FDs is compared using a standard shape database. Traditional interactive information retrieval systems function by creating inverted lists, or term indexes. For every term in the vocabulary, a list is created that contains the documents in which that term occurs and its relative frequency within each document. Retrieval algorithms then use these term frequencies alongside other collection statistics to identify the matching documents for a query. In this paper, we turn the process around: instead of indexing documents, we index query result sets. First, queries are run through a chosen retrieval system. For each query, the resulting document IDs are treated as terms and the score or rank of the document is used as the frequency statistic. An index of documents retrieved by basis queries is created. We call this index a reverted index. With reverted indexes, standard retrieval algorithms can retrieve the matching queries (as results) for a set of documents (used as queries). These recovered queries can then be used to identify additional documents, or to aid the user in query formulation, selection, and feedback. Current document retrieval methods use a vector space similarity measure to give scores of relevance to documents when related to a specific query. The central problem with these methods is that they neglect any spatial information within the documents in question. We present a new method, called Fourier Domain Scoring (FDS), which takes advantage of this spatial information, via the Fourier transform, to give a more accurate ordering of relevance to a document set. We show that FDS gives an improvement in precision over the vector space similarity measures for the common case of Web like queries, and it gives similar results to the vector space measures for longer queries.