Automatic cover detection -- the task of finding in an audio database all the covers of one or several query tracks -- has long been seen as a challenging theoretical problem in the MIR community and as an acute practical problem for authors and composers societies. Original algorithms proposed for this task have proven their accuracy on small datasets, but are unable to scale up to modern real-life audio corpora. On the other hand, faster approaches designed to process thousands of pairwise comparisons resulted in lower accuracy, making them unsuitable for practical use. In this work, we propose a neural network architecture that is trained to represent each track as a single embedding vector. The computation burden is therefore left to the embedding extraction -- that can be conducted offline and stored, while the pairwise comparison task reduces to a simple Euclidean distance computation. We further propose to extract each track's embedding out of its dominant melody representation, obtained by another neural network trained for this task. We then show that this architecture improves state-of-the-art accuracy both on small and large datasets, and is able to scale to query databases of thousands of tracks in a few seconds.
Another type of method has been proposed to alleviate the cost of the comparison function and to shift the burden to the audio features extraction function -- which can be done offline and stored. The general principle is to encode each audio track as a single scalar or vector -- its embedding -- and to reduce the similarity computation to a simple Euclidean distance between embeddings. Originally, embeddings were for instance computed as a single hash encoding a succession of pitch landmarks @cite_43 , or as a vector obtained by PCA dimensionality reduction of a chromagram's 2D-DFT @cite_32 or with locality-sensitive hashing of melodic excerpts @cite_25 .
@cite_43 @cite_25 @cite_32
2097461116 1486009449
Searching audio collections using high-level musical descriptors is a difficult problem, due to the lack of reliable methods for extracting melody, harmony, rhythm, and other such descriptors from unstructured audio signals. In this paper, we present a novel approach to melody-based retrieval in audio collections. Our approach supports audio, as well as symbolic queries and ranks results according to melodic similarity to the query. We introduce a beat-synchronous melodic representation consisting of salient melodic lines, which are extracted from the analyzed audio signal. We propose the use of a 2D shift-invariant transform to extract shift-invariant melodic fragments from the melodic representation and demonstrate how such fragments can be indexed and stored in a song database. An efficient search algorithm based on locality-sensitive hashing is used to perform retrieval according to similarity of melodic fragments. On the cover song detection task, good results are achieved for audio, as well as for symbolic queries, while fast retrieval performance makes the proposed system suitable for retrieval in large databases. Learning to hash involves learning hash functions from a set of images for embedding high-dimensional visual descriptors into a similarity-preserving low-dimensional Hamming space. Most of existing methods resort to a single representation of images, that is, only one type of visual descriptors is used to learn a hash function to assign binary codes to images. However, images are often described by multiple different visual descriptors (such as SIFT, GIST, HOG), so it is desirable to incorporate these multiple representations into learning a hash function, leading to multi-view hashing. In this paper we present a sequential spectral learning approach to multi-view hashing where a hash function is sequentially determined by solving the successive maximization of local variances subject to decorrelation constraints. We compute multi-view local variances by α-averaging view-specific distance matrices such that the best averaged distance matrix is determined by minimizing its α-divergence from view-specific distance matrices. We also present a scalable implementation, exploiting a fast approximate k-NN graph construction method, in which α-averaged distances computed in small partitions determined by recursive spectral bisection are gradually merged in conquer steps until whole examples are used. Numerical experiments on Caltech-256, CIFAR-20, and NUS-WIDE datasets confirm the high performance of our method, in comparison to single-view spectral hashing as well as existing multi-view hashing methods. In many situations, such as in biometric applications, there is need to encrypt and “hide” data, while simultaneously permitting restricted computations on them. We present a method to securely determine the 2 distance between two signals if they are close enough. This method relies on a locality sensitive hashing scheme based on a secure modular embedding, computed using quantized random projections, being a generalization of previous work in the area. Secure Modular Hashes (SMH) extracted from the signals preserve information about the distance between the signals, hiding other characteristic from the signals. Theoretical properties state that the described scheme provides a mechanism to threshold how much information to reveal, and is also information theoretically secure above this threshold. Finally, experimental results reveal that distances computed from SMH vectors can effectively replace the actual Euclidean distances with minimal degradation. We present a novel method to securely determine whether two signals are similar to each other, and apply it to approximate nearest neighbor clustering. The proposed method relies on a locality sensitive hashing scheme based on a secure binary embedding, computed using quantized random projections. Hashes extracted from the signals preserve information about the distance between the signals, provided this distance is small enough. If the distance between the signals is larger than a threshold, then no information about the distance is revealed. Theoretical and experimental justification is provided for this property. Further, when the randomized embedding parameters are unknown, then the mutual information between the hashes of any two signals decays to zero exponentially fast as a function of the l 2 distance between the signals. Taking advantage of this property, we suggest that these binary hashes can be used to perform privacy-preserving nearest neighbor search with significantly lower complexity compared to protocols which use the actual signals. In recent years, binary code learning, a.k.a. hashing, has received extensive attention in large-scale multimedia retrieval. It aims to encode high-dimensional data points into binary codes, hence the original high-dimensional metric space can be efficiently approximated via Hamming space. However, most existing hashing methods adopted offline batch learning, which is not suitable to handle incremental datasets with streaming data or new instances. In contrast, the robustness of the existing online hashing remains as an open problem, while the embedding of supervised semantic information hardly boosts the performance of the online hashing, mainly due to the defect of unknown category numbers in supervised learning. In this paper, we propose an online hashing scheme, termed Hadamard Codebook based Online Hashing (HCOH), which aims to solving the above problems towards robust and supervised online hashing. In particular, we first assign an appropriate high-dimensional binary codes to each class label, which is generated randomly by Hadamard codes. Subsequently, LSH is adopted to reduce the length of such Hadamard codes in accordance with the hash bits, which can adapt the predefined binary codes online, and theoretically guarantee the semantic similarity. Finally, we consider the setting of stochastic data acquisition, which facilitates our method to efficiently learn the corresponding hashing functions via stochastic gradient descend (SGD) online. Notably, the proposed HCOH can be embedded with supervised labels and is not limited to a predefined category number. Extensive experiments on three widely-used benchmarks demonstrate the merits of the proposed scheme over the state-of-the-art methods. Hashing has shown its efficiency and effectiveness in facilitating large-scale multimedia applications. Supervised knowledge (, semantic labels or pair-wise relationship) associated to data is capable of significantly improving the quality of hash codes and hash functions. However, confronted with the rapid growth of newly-emerging concepts and multimedia data on the Web, existing supervised hashing approaches may easily suffer from the scarcity and validity of supervised information due to the expensive cost of manual labelling. In this paper, we propose a novel hashing scheme, termed (ZSH), which compresses images of "unseen" categories to binary codes with hash functions learned from limited training data of "seen" categories. Specifically, we project independent data labels (i.e., 0 1-form label vectors) into semantic embedding space, where semantic relationships among all the labels can be precisely characterized and thus seen supervised knowledge can be transferred to unseen classes. Moreover, in order to cope with the semantic shift problem, we rotate the embedded space to more suitably align the embedded semantics with the low-level visual feature space, thereby alleviating the influence of semantic gap. In the meantime, to exert positive effects on learning high-quality hash functions, we further propose to preserve local structural property and discrete nature in binary codes. Besides, we develop an efficient alternating algorithm to solve the ZSH model. Extensive experiments conducted on various real-life datasets show the superior zero-shot image retrieval performance of ZSH as compared to several state-of-the-art hashing methods. We investigate the problem of matching symbolic representations directly to audio based representations for applications that use data from both domains. One such application is score alignment, which aligns a sequence of frames based on features such as chroma vectors and distance functions such as Euclidean distance. Good representations are critical, yet current systems use ad hoc constructions such as the chromagram that have been shown to work quite well. We investigate ways to learn chromagram-like representations that optimize the classification of “matching” vs. “non-matching” frame pairs of audio and MIDI. New representations learned automatically from examples not only perform better than the chromagram representation but they also reveal interesting projection structures that differ distinctly from the traditional chromagram. We introduce a method that enables scalable image search for learned metrics. Given pairwise similarity and dissimilarity constraints between some images, we learn a Mahalanobis distance function that captures the imagespsila underlying relationships well. To allow sub-linear time similarity search under the learned metric, we show how to encode the learned metric parameterization into randomized locality-sensitive hash functions. We further formulate an indirect solution that enables metric learning and hashing for vector spaces whose high dimensionality make it infeasible to learn an explicit weighting over the feature dimensions. We demonstrate the approach applied to a variety of image datasets. Our learned metrics improve accuracy relative to commonly-used metric baselines, while our hashing construction enables efficient indexing with learned distances and very large databases. This paper addresses the problem of designing binary codes for high-dimensional data such that vectors that are similar in the original space map to similar binary strings. We introduce a simple distribution-free encoding scheme based on random projections, such that the expected Hamming distance between the binary codes of two vectors is related to the value of a shift-invariant kernel (e.g., a Gaussian kernel) between the vectors. We present a full theoretical analysis of the convergence properties of the proposed scheme, and report favorable experimental performance as compared to a recent state-of-the-art method, spectral hashing. Fast retrieval methods are critical for large-scale and data-driven vision applications. Recent work has explored ways to embed high-dimensional features or complex distance functions into a low-dimensional Hamming space where items can be efficiently searched. However, existing methods do not apply for high-dimensional kernelized data when the underlying feature embedding for the kernel is unknown. We show how to generalize locality-sensitive hashing to accommodate arbitrary kernel functions, making it possible to preserve the algorithm's sub-linear time similarity search guarantees for a wide class of useful similarity functions. Since a number of successful image-based kernels have unknown or incomputable embeddings, this is especially valuable for image retrieval tasks. We validate our technique on several large-scale datasets, and show that it enables accurate and fast performance for example-based object classification, feature matching, and content-based retrieval. We introduce a method that enables scalable similarity search for learned metrics. Given pairwise similarity and dissimilarity constraints between some examples, we learn a Mahalanobis distance function that captures the examples' underlying relationships well. To allow sublinear time similarity search under the learned metric, we show how to encode the learned metric parameterization into randomized locality-sensitive hash functions. We further formulate an indirect solution that enables metric learning and hashing for vector spaces whose high dimensionality makes it infeasible to learn an explicit transformation over the feature dimensions. We demonstrate the approach applied to a variety of image data sets, as well as a systems data set. The learned metrics improve accuracy relative to commonly used metric baselines, while our hashing construction enables efficient indexing with learned distances and very large databases. In computer vision there has been increasing interest in learning hashing codes whose Hamming distance approximates the data similarity. The hashing functions play roles in both quantizing the vector space and generating similarity-preserving codes. Most existing hashing methods use hyper-planes (or kernelized hyper-planes) to quantize and encode. In this paper, we present a hashing method adopting the k-means quantization. We propose a novel Affinity-Preserving K-means algorithm which simultaneously performs k-means clustering and learns the binary indices of the quantized cells. The distance between the cells is approximated by the Hamming distance of the cell indices. We further generalize our algorithm to a product space for learning longer codes. Experiments show our method, named as K-means Hashing (KMH), outperforms various state-of-the-art hashing encoding methods. In this paper, we propose a cost-sensitive local binary feature learning (CS-LBFL) method for facial age estimation. Unlike the conventional facial age estimation methods that employ hand-crafted descriptors or holistically learned descriptors for feature representation, our CS-LBFL method learns discriminative local features directly from raw pixels for face representation. Motivated by the fact that facial age estimation is a cost-sensitive computer vision problem and local binary features are more robust to illumination and expression variations than holistic features, we learn a series of hashing functions to project raw pixel values extracted from face patches into low-dimensional binary codes, where binary codes with similar chronological ages are projected as close as possible, and those with dissimilar chronological ages are projected as far as possible. Then, we pool and encode these local binary codes within each face image as a real-valued histogram feature for face representation. Moreover, we propose a cost-sensitive local binary multi-feature learning method to jointly learn multiple sets of hashing functions using face patches extracted from different scales to exploit complementary information. Our methods achieve competitive performance on four widely used face aging data sets. A classic result of Johnson and Lindenstrauss asserts that any set of n points in d-dimensional Euclidean space can be embedded into k-dimensional Euclidean space---where k is logarithmic in n and independent of d--so that all pairwise distances are maintained within an arbitrarily small factor. All known constructions of such embeddings involve projecting the n points onto a spherically random k-dimensional hyperplane through the origin. We give two constructions of such embeddings with the property that all elements of the projection matrix belong in -1, 0, +1 . Such constructions are particularly well suited for database environments, as the computation of the embedding reduces to evaluating a single aggregate over k random partitions of the attributes. We map categorical variables in a function approximation problem into Euclidean spaces, which are the entity embeddings of the categorical variables. The mapping is learned by a neural network during the standard supervised training process. Entity embedding not only reduces memory usage and speeds up neural networks compared with one-hot encoding, but more importantly by mapping similar values close to each other in the embedding space it reveals the intrinsic properties of the categorical variables. We applied it successfully in a recent Kaggle competition and were able to reach the third position with relative simple features. We further demonstrate in this paper that entity embedding helps the neural network to generalize better when the data is sparse and statistics is unknown. Thus it is especially useful for datasets with lots of high cardinality features, where other methods tend to overfit. We also demonstrate that the embeddings obtained from the trained neural network boost the performance of all tested machine learning methods considerably when used as the input features instead. As entity embedding defines a distance measure for categorical variables it can be used for visualizing categorical data and for data clustering. What makes images similar? To measure the similarity between images, they are typically embedded in a feature-vector space, in which their distance preserve the relative dissimilarity. However, when learning such similarity embeddings the simplifying assumption is commonly made that images are only compared to one unique measure of similarity. A main reason for this is that contradicting notions of similarities cannot be captured in a single space. To address this shortcoming, we propose Conditional Similarity Networks (CSNs) that learn embeddings differentiated into semantically distinct subspaces that capture the different notions of similarities. CSNs jointly learn a disentangled embedding where features for different similarities are encoded in separate dimensions as well as masks that select and reweight relevant dimensions to induce a subspace that encodes a specific similarity notion. We show that our approach learns interpretable image representations with visually relevant semantic subspaces. Further, when evaluating on triplet questions from multiple similarity notions our model even outperforms the accuracy obtained by training individual specialized networks for each notion separately. Over the past few years, some embedding methods have been proposed for feature extraction and dimensionality reduction in various machine learning and pattern classification tasks. Among the methods proposed are Neighborhood Preserving Embedding (NPE), Locality Preserving Projection (LPP) and Local Discriminant Embedding (LDE) which have been used in such applications as face recognition and image video retrieval. However, although the data in these applications are more naturally represented as higher-order tensors, the embedding methods can only work with vectorized data representations which may not capture well some useful information in the original data. Moreover, high-dimensional vectorized representations also suffer from the curse of dimensionality and the high computational demand. In this paper, we propose some novel tensor embedding methods which, unlike previous methods, take data directly in the form of tensors of arbitrary order as input. These methods allow the relationships between dimensions of a tensor representation to be efficiently characterized. Moreover, they also allow the intrinsic local geometric and topological properties of the manifold embedded in a tensor space to be naturally estimated. Furthermore, they do not suffer from the curse of dimensionality and the high computational demand. We demonstrate the effectiveness of the proposed tensor embedding methods on a face recognition application and compare them with some previous methods. Extensive experiments show that our methods are not only more effective but also more efficient. Learning based hashing methods have attracted considerable attention due to their ability to greatly increase the scale at which existing algorithms may operate. Most of these methods are designed to generate binary codes that preserve the Euclidean distance in the original space. Manifold learning techniques, in contrast, are better able to model the intrinsic structure embedded in the original high-dimensional data. The complexity of these models, and the problems with out-of-sample data, have previously rendered them unsuitable for application to large-scale embedding, however. In this work, we consider how to learn compact binary embeddings on their intrinsic manifolds. In order to address the above-mentioned difficulties, we describe an efficient, inductive solution to the out-of-sample data problem, and a process by which non-parametric manifold learning may be used as the basis of a hashing method. Our proposed approach thus allows the development of a range of new hashing techniques exploiting the flexibility of the wide variety of manifold learning approaches available. We particularly show that hashing on the basis of t-SNE [29] outperforms state-of-the-art hashing methods on large-scale benchmark datasets, and is very effective for image classification with very short code lengths. Even if, most of 2D face recognition approaches reached recognition rate more than 90 in controlled environment, current days face recognition systems degrade their performance in case of uncontrolled environment which includes pose variations, illumination variations, expression variations and ageing effect etc. Inclusion of 3D face analysis gives an age over 2D face recognition as they give vital informations such as 3D shape, texture and depth which improve discrimination power of an algorithm. In this paper, we have investigated different 3D face recognition approaches that are robust to changes in facial expressions and illumination variations. 2D-PCA and 2D-LDA approaches have been extended to 3D face recognition because they can directly work on 2D depth image matrices rather than 1D vectors without need for transformations before feature extraction. In turn, this reduces storage space and time required for computations. 2D depth image is extracted from 3D face model and nose region from depth mapped image has been detected as a reference point for cropping stage to convert model into a standard size. Two Dimensional Principal Component Analysis (2D-PCA) and Two Dimensional Linear Discriminant analysis (2D-LDA) are employed to obtain feature vectors globally compared to feature vectors obtained locally using PCA or LDA. Finally, euclidean distance classifier is applied for comparison of extracted features. A set of experiments on GavabDB 3D face database, which has 61 individuals in total, demonstrated that 3D face recognition using 2D-LDA method has achieved recognition accuracy of 93.3 and EER of 8.96 over database, which is higher compared to 2D-PCA. So, more optimized performance has been achieved using 2D-LDA for 3D face recognition analysis. Structural pattern recognition approaches offer the most expressive, convenient, powerful but computational expensive representations of underlying relational information. To benefit from mature, less expensive and efficient state-of-the-art machine learning models of statistical pattern recognition they must be mapped to a low-dimensional vector space. Our method of explicit graph embedding bridges the gap between structural and statistical pattern recognition. We extract the topological, structural and attribute information from a graph and encode numeric details by fuzzy histograms and symbolic details by crisp histograms. The histograms are concatenated to achieve a simple and straightforward embedding of graph into a low-dimensional numeric feature vector. Experimentation on standard public graph datasets shows that our method outperforms the state-of-the-art methods of graph embedding for richly attributed graphs. Highlights? We propose an explicit graph embedding method. ? We perform multilevel analysis of graph to extract global, topological structural and attribute information. ? We use homogeneity of subgraphs in graph for extracting topological structural details. ? We encode numeric information by fuzzy histograms and symbolic information by crisp histograms. ? Our method outperforms graph embedding methods for richly attributed graphs.