In recent years, the creation of block-structured dictionary has attracted a lot of interest. Learning such dictionaries involve two step process: block formation and dictionary update. Both these steps are important in producing an effective dictionary. The existing works mostly assume that the block structure is known a priori while learning the dictionary. For finding the unknown block struc...

A subordination dictionary is important in a number of text processing applications. We present a method for generating such dictionary for Russian verbs using Google Books Ngram data. An intended purpose of the dictionary is an event extraction system for Russian that uses the dictionary to define extraction patterns.

Title of dissertation: SPARSE DICTIONARY LEARNING AND DOMAIN ADAPTATION FOR FACE AND ACTION RECOGNITION Qiang Qiu, Doctor of Philosophy, 2013 Dissertation directed by: Professor Rama Chellappa Department of Computer Science New approaches for dictionary learning and domain adaptation are proposed for face and action recognition. We first present an approach for dictionary learning of action att...

For object classification in video surveillance, features extracted from images are compared with a visual dictionary. The best-matching features are learned by the classifier to determine the object class. In this paper, the visual dictionary concept is extended with Interest Point Operators (IPOs). In a first experiment, the influence of using IPOs on the visual dictionary creation process is...

In this work, we propose a system for automatic music transcription which adapts dictionary templates so that they closely match the spectral shape of the instrument sources present in each recording. Current dictionary-based automatic transcription systems keep the input dictionary fixed, thus the spectral shape of the dictionary components might not match the shape of the test instrument sour...

We introduce a coe cient update procedure into existing batch and online dictionary learning algorithms. We rst propose an algorithm which is a coe cient updated version of the Method of Optimal Directions (MOD) dictionary learning algorithm (DLA). The MOD algorithm with coe cient updates presents a computationally expensive dictionary learning iteration with high convergence rate. Secondly, we...

Dictionary learning algorithms for audio modeling typically learn a dictionary such that each time frame of the given sound source is approximately equal to a linear combination of the dictionary elements. Since audio is non-stationary data, learning a single dictionary to explain all time frames of the sound source might not be the best modeling strategy. We therefore recently proposed a techn...

This paper presents a fast method for sparse decomposition of real second-order polynomial phase signals (PPSs). In the method, we first set three concatenate dictionaries to complete the sparse decomposition of real second-order polynomial phase signals. Three concatenate dictionaries are the frequency modulation dictionary, the frequency dictionary and the phase dictionary, respectively. Seco...

This letter considers the problem of dictionary learning for sparse signal representation whose atoms have low mutual coherence. To learn such dictionaries, at each step, we first update the dictionary using the method of optimal directions (MOD) and then apply a dictionary rank shrinkage step to decrease its mutual coherence. In the rank shrinkage step, we first compute a rank 1 decomposition ...

We consider the problem of learning sparsely used overcomplete dictionaries, where each observation is a sparse combination of elements from an unknown overcomplete dictionary. We establish exact recovery when the dictionary elements are mutually incoherent. Our method consists of a clustering-based initialization step, which provides an approximate estimate of the true dictionary with guarante...