Vector Quantization Techniques for Approximate Nearest Neighbor Search on Large-Scale Datasets

The technological developments of the last twenty years are leading the world to a new era. The invention of the internet, mobile phones and smart devices are resulting in an exponential increase in data. As the data is growing every day, finding similar patterns or matching samples to a query is no longer a simple task because of its computational costs and storage limitations. Special signal processing techniques are required in order to handle the growth in data, as simply adding more and more computers cannot keep up.Nearest neighbor search, or similarity search, proximity search or near item search is the problem of finding an item that is nearest or most similar to a query according to a distance or similarity measure. When the reference set is very large, or the distance or similarity calculation is complex, performing the nearest neighbor search can be computationally demanding. Considering today’s ever-growing datasets, where the cardinality of samples also keep increasing, a growing interest towards approximate methods has emerged in the research community.Vector Quantization for Approximate Nearest Neighbor Search (VQ for ANN) has proven to be one of the most efficient and successful methods targeting the aforementioned problem. It proposes to compress vectors into binary strings and approximate the distances between vectors using look-up tables. With this approach, the approximation of distances is very fast, while the storage space requirement of the dataset is minimized thanks to the extreme compression levels. The distance approximation performance of VQ for ANN has been shown to be sufficiently well for retrieval and classification tasks demonstrating that VQ for ANN techniques can be a good replacement for exact distance calculation methods.This thesis contributes to VQ for ANN literature by proposing five advanced techniques, which aim to provide fast and efficient approximate nearest neighbor search on very large-scale datasets. The proposed methods can be divided into two groups. The first group consists of two techniques, which propose to introduce subspace clustering to VQ for ANN. These methods are shown to give the state-of-the-art performance according to tests on prevalent large-scale benchmarks. The second group consists of three methods, which propose improvements on residual vector quantization. These methods are also shown to outperform their predecessors. Apart from these, a sixth contribution in this thesis is a demonstration of VQ for ANN in an application of image classification on large-scale datasets. It is shown that a k-NN classifier based on VQ for ANN performs on par with the k-NN classifiers, but requires much less storage space and computations

Competitive Quantization for Approximate Nearest Neighbor Search

In this study, we propose a novel vector quantization algorithm for Approximate Nearest Neighbor (ANN) search, based on a joint competitive learning strategy and hence called as competitive quantization (CompQ). CompQ is a hierarchical algorithm, which iteratively minimizes the quantization error by jointly optimizing the codebooks in each layer, using a gradient decent approach. An extensive set of experimental results and comparative evaluations show that CompQ outperforms the-state-of-the-art while retaining a comparable computational complexity.Scopu

Vector Quantization Techniques for Approximate Nearest Neighbor Search on Large-Scale Datasets

The technological developments of the last twenty years are leading the world to a new era. The invention of the internet, mobile phones and smart devices are resulting in an exponential increase in data. As the data is growing every day, finding similar patterns or matching samples to a query is no longer a simple task because of its computational costs and storage limitations. Special signal processing techniques are required in order to handle the growth in data, as simply adding more and more computers cannot keep up.Nearest neighbor search, or similarity search, proximity search or near item search is the problem of finding an item that is nearest or most similar to a query according to a distance or similarity measure. When the reference set is very large, or the distance or similarity calculation is complex, performing the nearest neighbor search can be computationally demanding. Considering today’s ever-growing datasets, where the cardinality of samples also keep increasing, a growing interest towards approximate methods has emerged in the research community.Vector Quantization for Approximate Nearest Neighbor Search (VQ for ANN) has proven to be one of the most efficient and successful methods targeting the aforementioned problem. It proposes to compress vectors into binary strings and approximate the distances between vectors using look-up tables. With this approach, the approximation of distances is very fast, while the storage space requirement of the dataset is minimized thanks to the extreme compression levels. The distance approximation performance of VQ for ANN has been shown to be sufficiently well for retrieval and classification tasks demonstrating that VQ for ANN techniques can be a good replacement for exact distance calculation methods.This thesis contributes to VQ for ANN literature by proposing five advanced techniques, which aim to provide fast and efficient approximate nearest neighbor search on very large-scale datasets. The proposed methods can be divided into two groups. The first group consists of two techniques, which propose to introduce subspace clustering to VQ for ANN. These methods are shown to give the state-of-the-art performance according to tests on prevalent large-scale benchmarks. The second group consists of three methods, which propose improvements on residual vector quantization. These methods are also shown to outperform their predecessors. Apart from these, a sixth contribution in this thesis is a demonstration of VQ for ANN in an application of image classification on large-scale datasets. It is shown that a k-NN classifier based on VQ for ANN performs on par with the k-NN classifiers, but requires much less storage space and computations

Televizyon yayınlarında ses olay tespiti.

The availability of digital media has grown tremendously with the fast-paced ever-growing storage and communication technologies. As a result, today, we are facing a problem in indexing and browsing the huge amounts of multimedia data. This amount of data is impossible to be indexed or browsed by hand so automatic indexing and browsing systems are proposed. Audio Event Detection is a research area which tries to analyse the audio data in a semantic and perceptual manner, to bring a conceptual solution to this problem. In this thesis, a method for detecting several audio events in TV broadcast is proposed. The proposed method includes an audio segmentation stage to detect event boundaries. Broadcast audio is classified into 17 classes. The feature set for each event is obtained by using a feature selection algorithm to select suitable features among a large set of popular descriptors. Support Vector Machines and Gaussian Mixture Models are used as classifiers and the proposed system achieved an average recall rate of 88% for 17 different audio events. Comparing with the results in the literature, the proposed method is promising.M.S. - Master of Scienc

Filterbank Learning for Deep Neural Network Based Polyphonic Sound Event Detection

Deep learning techniques such as deep feedforward neural networks and deep convolutional neural networks have recently been shown to improve the performance in sound event detection compared to traditional methods such as Gaussian mixture models. One of the key factors of this improvement is the capability of deep architectures to automatically learn higher levels of acoustic features in each layer. In this work, we aim to combine the feature learning capabilities of deep architectures with the empirical knowledge of human perception. We use the first layer of a deep neural network to learn a mapping from a high-resolution magnitude spectrum to smaller amount of frequency bands, which effectively learns a filterbank for the sound event detection task. We initialize the first hidden layer weights to match with the perceptually motivated mel filterbank magnitude response. We also integrate this initialization scheme with context windowing by using an appropriately constrained deep convolutional neural network. The proposed method does not only result with better detection accuracy, but also provides insight on the frequencies deemed essential for better discrimination of given sound events.acceptedVersionPeer reviewe

Joint K-Means quantization for Approximate Nearest Neighbor Search

Recently, Approximate Nearest Neighbor (ANN) Search has become a very popular approach for similarity search on large-scale datasets. In this paper, we propose a novel vector quantization method for ANN, which introduces a joint multi-layer K-Means clustering solution for determination of the codebooks. The performance of the proposed method is improved further by a joint encoding scheme. Experimental results verify the success of the proposed algorithm as it outperforms the state-of-the-art methods.Scopu

Extended quantum cuts for unsupervised salient object extraction

In this manuscript, an unsupervised salient object extraction algorithm is proposed for RGB and RGB-Depth images. Saliency estimation is formulated as a foreground detection problem. To this end, Quantum-Cuts (QCUT), a recently proposed spectral foreground detection method is investigated and extended to formulate the saliency estimation problem more efficiently. The contributions of this work are as follows: (1) a new proof for QCUT from spectral graph theory point of view is provided, (2) a detailed analysis of QCUT and comparison to well-known graph clustering methods are conducted, (3) QCUT is utilized in a multiresolution framework, (4) a novel affinity matrix construction scheme is proposed for better encoding of saliency cues into the graph representation and (5) a multispectral analysis for a richer set of salient object proposals is investigated. With the above improvements, we propose Extended Quantum Cuts, which consistently achieves an exquisite performance over all benchmark saliency detection datasets, containing around 18 k images in total. Finally, the proposed approach also outperforms the state-of-the-art on a recently announced RGB-Depth saliency dataset.Scopu

Self-organizing binary encoding for approximate nearest neighbor search

Approximate Nearest Neighbor (ANN) search for indexing and retrieval has become very popular with the recent growth of the databases in both size and dimension. In this paper, we propose a novel method for fast approximate distance calculation among the compressed samples. Inspiring from Kohonen's self-organizing maps, we propose a structured hierarchical quantization scheme in order to compress database samples in a more efficient way. Moreover, we introduce an error correction stage for encoding, which further improves the performance of the proposed method. The results on publicly available benchmark datasets demonstrate that the proposed method outperforms many well-known methods with comparable computational cost and storage space.Scopu

An optimized k-NN approach for classification on imbalanced datasets with missing data

In this paper, we describe our solution for the machine learning prediction challenge in IDA 2016. For the given problem of 2-class classification on an imbalanced dataset with missing data, we first develop an imputation method based on k-NN to estimate the missing values. Then we define a tailored representation for the given problem as an optimization scheme, which consists of learned distance and voting weights for k-NN classification. The proposed solution performs better in terms of the given challenge metric compared to the traditional classification methods such as SVM, AdaBoost or Random Forests. Springer International Publishing AG 2016.Scopu

An unsupervised audio segmentation method using Bayesian information criterion

Audio segmentation is a well-known problem which can be considered from various angles. In the context of this paper, audio segmentation problem is to extract small "homogeneous" pieces of audio in which the content does not change in terms of the present audio events. The proposed method is compared with the well-known segmentation method; Bayesian Information Criterion (BIC) based Divide-and-Conquer, in terms of average segment duration and computational complexity