This work proposes a method which enables us to perform kernel Fisher discriminant analysis in the whole eigenspace for face recognition. It employs the ratio of eigenvalues to decompose the entire kernel feature space into two subspaces: a reliable subspace spanned mainly by the facial variation and an unreliable subspace due to finite number of training samples. Eigenvectors are then scaled u...

Audio-to-audio alignment is the task of synchronizing two audio sequences with similar musical content in time. We investigated a large set of audio features for this task. The features were chosen to represent four different content-dependent similarity categories: the envelope, the timbre, note-onsets and the pitch. The features were subjected to two processing stages. First, a feature subset...

Some instructors record letter grades for tests and assignments, and others record numerical values, often the percent correct on tests. Later, under either method, the grades are averaged, often employing a weighting process designed to make some grades count more heavily than others. Discussion of the merits of different approaches usually centers around the question of whether it is better t...

Feature combination is a popular method for improving object classification performances. In this paper we present a simple and effective weighting scheme for feature combination based on the dominant-set notion of a cluster. Specifically, we use dominant sets clustering to evaluate how accurate a kernel matrix is expected to be for a SVM classifier. This expected kernel accuracy reflects the d...

In this paper we propose a generic framework for the optimization of image feature encoders for image retrieval. Our approach uses a triplet-based objective that compares, for a given query image, the similarity scores of an image with a matching and a non-matching image, penalizing triplets that give a higher score to the non-matching image. We use stochastic gradient descent to address the re...

This MIREX submission for symbolic music similarity task adopts textual information retrieval methodology in the process of music information retrieval. The main contribution of this approach is to utilize well established term weighting methods for text retrieval and check their suitability for music data. We use a simple feature extraction method, so that the performance of an algorithm depen...

Feature reduction are common techniques that used to improve the efficiency and accuracy of the document classification systems. The problems associated with these techniques are the highly dimensionality of the feature space and The difficulty of selecting the important features for understanding the document in question. The document usually consists of several parts and the important feature...

Several methods for feature selection and weighting have been implemented and tested within the similarity-based framework of classification methods. Features are excluded and ranked according to their contribution to the classification accuracy in the crossvalidation tests. Weighting factors used to compute distances are optimized using global minimization procedures or search-based methods. O...

In this paper we review the feature selection and representation techniques in CBIR systems, and propose a unified feature representation paradigm. We revise our previously proposed water-filling edge features with newly proposed primitives and present them using this unified feature formation paradigm. Multi-scale feature formation is proposed to support cross-resolution image matching. Sub-im...