[Show abstract][Hide abstract] ABSTRACT: In this paper, we employ a linguistic-processing approach to the content-based retrieval of music information. Central to the approach is the use of a lossy version of the Lempel-Ziv incremental parsing (LZIP) algorithm, which constructs a dictionary by incrementally parsing music feature vectors. LZIP is adopted as a source characterization technique owing to it's universal-coding nature, and asymptotic convergence to the entropy of the source. The dictionary is composed of variable-length parsed representations, which are used to construct a highly sparse co-occurrence matrix, which counts the occurrence of the parsed representations in each music. As a feature analysis framework, Latent Semantic Analysis (LSA) is then applied to the co-occurrence matrix to generate a lower-dimensional approximation that exposes the most salient features of the represented audio documents. The aforementioned approach, in addition to adopting reduced sampling rates and quantized feature vectors, yields a system with reduced requirements in terms of processing and storage, and increases the tolerance to noisy queries. We demonstrate the performance of the system in the music genre classification problem, and analyze its robustness to perturbed queries. Moreover, we demonstrate that using the incremental parsing algorithm in forming the audio dictionary has superior retrieval performance compared to techniques yielding a dictionary with fixed-length entries such as vector quantization.
[Show abstract][Hide abstract] ABSTRACT: We apply pattern recognition techniques to enhance the robustness of moment-invariants-based image classifiers. Moment invariants exhibit variations under transformations that do not preserve the original image function, such as geometrical transformations involving interpolation. Such variations degrade the performance of classifiers due to the errors in the nearest neighbor search stage. We propose the use of linear discriminant analysis (LDA) and principal component analysis (PCA) to alleviate the variations and enhance the robustness of classification. We demonstrate the improved performance in image registration applications under spatial scaling and rotation transformations.