We consider a natural generalization of the classical pattern matching problem: given compressed representations of a pattern p[1. . M ] and a text t[1. . N ] of sizes m and n, respectively, does p occur in t? We develop an optimal linear time solution for the case when p and t are compressed using the LZW method. This improves the previously known O((n + m) log(n + m)) time solution of G asien...

The problem of compressed pattern matching, which has recently been treated in many papers dealing with free text, is extended to structured files, specifically to dictionaries, which appear in any full-text retrieval system. The prefix-omission method is combined with Huffman coding and a new variant based on Fibonacci codes is presented. Experimental results suggest that the new methods are o...

In this paper we focus on the problem of compressed pattern matching for the text compression using antidictionaries, which is a new compression scheme proposed recently by Crochemore et al. (1998). We show an algorithm which preprocesses a pattern of length m and an antidictionary M in O(m 2 + kMk) time, and then scans a compressed text of length n in O(n+ r) time to nd all pattern occurrences...

The task of compressed pattern matching [2] is to report all the occurences of a given pattern P in a text T available in compressed form. Certain compression algorithms allow for searching without prior decoding which may be practical, especially if the search is faster than in the non-compressed representation. Most of the known schemes, however, either assume a text formed into words, or are...

Sequitur due to Nevill-Manning and Witten. [18] is a powerful program to infer a phrase hierarchy from the input text, that also provides extremely effective compression of large quantities of semi-structured text [17]. In this paper, we address the problem of searching in Sequitur compressed text directly. We show a compressed pattern matching algorithm that finds a pattern in compressed text ...

ÐTo achieve integrated segmentation and recognition in complex scenes, the model-based approach has widely been accepted as a promising paradigm. However, the performance is still far from satisfactory when the target object is highly deformed and the level of outlier contamination is high. In this paper, we first describe two Bayesian frameworks, one for classifying input patterns and another ...