Approximate string matching

About: Approximate string matching is a research topic. Over the lifetime, 1903 publications have been published within this topic receiving 62352 citations. The topic is also known as: fuzzy string-searching algorithm & fuzzy string-matching algorithm.

String Matching with Metric Trees Using an Approximate Distance

Abstract: Searching in a large data set those strings that are more similar, according to the edit distance, to a given one is a time-consuming process. In this paper we investigate the performance of metric trees, namely the M-tree, when they are extended using a cheap approximate distance function as a filter to quickly discard irrelevant strings. Using the bag distance as an approximation of the edit distance, we show an improvement in performance up to 90% with respect to the basic case. This, along with the fact that our solution is independent on both the distance used in the pre-test and on the underlying metric index, demonstrates that metric indices are a powerful solution, not only for many modern application areas, as multimedia, data mining and pattern recognition, but also for the string matching problem.

More Efficient Algorithms for Closest String and Substring Problems

Abstract: The closest string problem and the closest substring problem are all natural theoretical computer science problems and find important applications in computational biology. Given $n$ input strings, the closest string (substring) problem finds a new string within distance $d$ to (a substring of) each input string and such that $d$ is minimized. Both problems are NP-complete. In this paper we propose new algorithms for these two problems. For the closest string problem, we developed an exact algorithm with time complexity $O(n|\Sigma|^{O(d)})$, where $\Sigma$ is the alphabet. This improves the previously best known result $O(nd^{O(d)})$ and results into a polynomial time algorithm when $d=O(\log n)$. By using this algorithm, a polynomial time approximation scheme (PTAS) for the closest string problem is also given with time complexity $O(n^{O(\epsilon^{-2})})$, improving the previously best known $O(n^{O(\epsilon^{-2}\log\frac{1}{\epsilon})})$ PTAS. A new algorithm for the closest substring problem is also proposed. Finally, we prove that a restricted version of the closest substring problem has the same parameterized complexity as the closest substring, answering an open question in the literature.

Fast algorithms for top-k approximate string matching

Abstract: Top-k approximate querying on string collections is an important data analysis tool for many applications, and it has been exhaustively studied However, the scale of the problem has increased dramatically because of the prevalence of the Web In this paper, we aim to explore the efficient top-k similar string matching problem Several efficient strategies are introduced, such as length aware and adaptive q-gram selection We present a general q-gram based framework and propose two efficient algorithms based on the strategies introduced Our techniques are experimentally evaluated on three real data sets and show a superior performance

A method to infer emotions from facial Action Units

Abstract: We present a robust method to map detected facial Action Units (AUs) to six basic emotions. Automatic AU recognition is prone to errors due to illumination, tracking failures and occlusions. Hence, traditional rule based methods to map AUs to emotions are very sensitive to false positives and misses among the AUs. In our method, a set of chosen AUs are mapped to the six basic emotions using a learned statistical relationship and a suitable matching technique. Relationships between the AUs and emotions are captured as template strings comprising the most discriminative AUs for each emotion. The template strings are computed using a concept called discriminative power. The Longest Common Subsequence (LCS) distance, an approach for approximate string matching, is applied to calculate the closeness of a test string of AUs with the template strings, and hence infer the underlying emotions. LCS is found to be efficient in handling practical issues like erroneous AU detection and helps to reduce false predictions. The proposed method is tested with various databases like CK+, ISL, FACS, JAFFE, MindReading and many real-world video frames. We compare our performance with rule based techniques, and show clear improvement on both benchmark databases and real-world datasets.