Audio Content-Based Music Retrieval

The rapidly growing corpus of digital audio material requires novel retrieval strategies for exploring large music collections. Traditional retrieval strategies rely on metadata that describe the actual audio content in words. In the case that such textual descriptions are not available, one requires content-based retrieval strategies which only utilize the raw audio material. In this contribution, we discuss content-based retrieval strategies that follow the query-by-example paradigm: given an audio query, the task is to retrieve all documents that are somehow similar or related to the query from a music collection. Such strategies can be loosely classified according to their "specificity", which refers to the degree of similarity between the query and the database documents. Here, high specificity refers to a strict notion of similarity, whereas low specificity to a rather vague one. Furthermore, we introduce a second classification principle based on "granularity", where one distinguishes between fragment-level and document-level retrieval. Using a classification scheme based on specificity and granularity, we identify various classes of retrieval scenarios, which comprise "audio identification", "audio matching", and "version identification". For these three important classes, we give an overview of representative state-of-the-art approaches, which also illustrate the sometimes subtle but crucial differences between the retrieval scenarios. Finally, we give an outlook on a user-oriented retrieval system, which combines the various retrieval strategies in a unified framework

Detection on Straight Line Problem in Triangle Geometry Features for Digit Recognition

Geometric object especially triangle geometry has been widely used in digit recognition area. The triangle geometry properties have been implemented as the triangle features which are used to construct the triangle shape. Triangle is formed based on three points of triangle corner A, B and C. However, a problem occurs when three points of triangle corner were in parallel line. Thus, an algorithm has been proposed in order to solve the straight line problem. The Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) were used to measure based on the classification accuracy. Four datasets were used: HODA, IFCHDB, MNIST and BANGLA. The comparison results classification demonstrated the effectiveness of our proposed method

Empreintes audio et stratégies d'indexation associées pour l'identification audio à grande échelle

N this work we give a precise definition of large scale audio identification. In particular, we make a distinction between exact and approximate matching. In the first case, the goal is to match two signals coming from one same recording with different post-processings. In the second case, the goal is to match two signals that are musically similar. In light of these definitions, we conceive and evaluate different audio-fingerprint models.Dans cet ouvrage, nous définissons précisément ce qu’est l’identification audio à grande échelle. En particulier, nous faisons une distinction entre l’identification exacte, destinée à rapprocher deux extraits sonores provenant d’un même enregistrement, et l’identification approchée, qui gère également la similarité musicale entre les signaux. A la lumière de ces définitions, nous concevons et examinons plusieurs modèles d’empreinte audio et évaluons leurs performances, tant en identification exacte qu’en identificationapprochée

Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio

Biometrics based privacy-preserving authentication and mobile template protection

Smart mobile devices are playing a more and more important role in our daily life. Cancelable biometrics is a promising mechanism to provide authentication to mobile devices and protect biometric templates by applying a noninvertible transformation to raw biometric data. However, the negative effect of nonlinear distortion will usually degrade the matching performance significantly, which is a nontrivial factor when designing a cancelable template. Moreover, the attacks via record multiplicity (ARM) present a threat to the existing cancelable biometrics, which is still a challenging open issue. To address these problems, in this paper, we propose a new cancelable fingerprint template which can not only mitigate the negative effect of nonlinear distortion by combining multiple feature sets, but also defeat the ARM attack through a proposed feature decorrelation algorithm. Our work is a new contribution to the design of cancelable biometrics with a concrete method against the ARM attack. Experimental results on public databases and security analysis show the validity of the proposed cancelable template

An Overview of Advances of Pattern Recognition Systems in Computer Vision

26 pagesFirst of all, let's give a tentative answer to the following question: what is pattern recognition (PR)? Among all the possible existing answers, that which we consider being the best adapted to the situation and to the concern of this chapter is: "pattern recognition is the scientific discipline of machine learning (or artificial intelligence) that aims at classifying data (patterns) into a number of categories or classes". But what is a pattern? A pattern recognition system (PRS) is an automatic system that aims at classifying the input pattern into a specific class. It proceeds into two successive tasks: (1) the analysis (or description) that extracts the characteristics from the pattern being studied and (2) the classification (or recognition) that enables us to recognise an object (or a pattern) by using some characteristics derived from the first task

The Optimisation of Elementary and Integrative Content-Based Image Retrieval Techniques

Image retrieval plays a major role in many image processing applications. However, a number of factors (e.g. rotation, non-uniform illumination, noise and lack of spatial information) can disrupt the outputs of image retrieval systems such that they cannot produce the desired results. In recent years, many researchers have introduced different approaches to overcome this problem. Colour-based CBIR (content-based image retrieval) and shape-based CBIR were the most commonly used techniques for obtaining image signatures. Although the colour histogram and shape descriptor have produced satisfactory results for certain applications, they still suffer many theoretical and practical problems. A prominent one among them is the well-known “curse of dimensionality “. In this research, a new Fuzzy Fusion-based Colour and Shape Signature (FFCSS) approach for integrating colour-only and shape-only features has been investigated to produce an effective image feature vector for database retrieval. The proposed technique is based on an optimised fuzzy colour scheme and robust shape descriptors. Experimental tests were carried out to check the behaviour of the FFCSS-based system, including sensitivity and robustness of the proposed signature of the sampled images, especially under varied conditions of, rotation, scaling, noise and light intensity. To further improve retrieval efficiency of the devised signature model, the target image repositories were clustered into several groups using the k-means clustering algorithm at system runtime, where the search begins at the centres of each cluster. The FFCSS-based approach has proven superior to other benchmarked classic CBIR methods, hence this research makes a substantial contribution towards corresponding theoretical and practical fronts

On the Performance Improvement of Iris Biometric System

Iris is an established biometric modality with many practical applications. Its performance is influenced by noise, database size, and feature representation. This thesis focusses on mitigating these challenges by efficiently characterising iris texture,developing multi-unit iris recognition, reducing the search space of large iris databases, and investigating if iris pattern change over time.To suitably characterise texture features of iris, Scale Invariant Feature Transform (SIFT) is combined with Fourier transform to develop a keypoint descriptor-F-SIFT. Proposed F-SIFT is invariant to transformation, illumination, and occlusion along with strong texture description property. For pairing the keypoints from gallery and probe iris images, Phase-Only Correlation (POC) function is used. The use of phase information reduces the wrong matches generated using SIFT. Results demonstrate the effectiveness of F-SIFT over existing keypoint descriptors.To perform the multi-unit iris fusion, a novel classifier is proposed known as Incremental Granular Relevance Vector Machine (iGRVM) that incorporates incremental and granular learning into RVM. The proposed classifier by design is scalable and unbiased which is particularly suitable for biometrics. The match scores from individual units of iris are passed as an input to the corresponding iGRVM classifier, and the posterior probabilities are combined using weighted sum rule. Experimentally, it is shown that the performance of multi-unit iris recognition improves over single unit iris. For search space reduction, local feature based indexing approaches are developed using multi-dimensional trees. Such features extracted from annular iris images are used to index the database using k-d tree. To handle the scalability issue of k-d tree, k-d-b tree based indexing approach is proposed. Another indexing approach using R-tree is developed to minimise the indexing errors. For retrieval, hybrid coarse-to-fine search strategy is proposed. It is inferred from the results that unification of hybrid search with R-tree significantly improves the identification performance. Iris is assumed to be stable over time. Recently, researchers have reported that false rejections increase over the period of time which in turn degrades the performance. An empirical investigation has been made on standard iris aging databases to find whether iris patterns change over time. From the results, it is found that the rejections are primarily due to the presence of other covariates such as blur, noise, occlusion, pupil dilation, and not due to agin