Fast large-scale multimedia indexing and searching

Hammami, Mohamed; Osipyan, Hasmik; Marchand-Maillet, Stéphane

doi:10.1109/cbmi.2014.6849819

Cited by 3 publications

(3 citation statements)

References 16 publications

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“…Several GPU implementations of widely used algorithms such as k-nearest neighbor [10] and LSH [11] have been proposed and can be adopted for copy detection systems. A GPU implementation of the Metric Permutation Table algorithm is proposed in [12] to speed up the search of digital images. In [13], Mel-Frequency Cepstral Coefficients plus energy and its delta coefficients are used as audio features.…”

Section: Introductionmentioning

confidence: 99%

GPU implementation of an audio fingerprints similarity search algorithm

Ouali

Dumouchel

Gupta

2015

2015 13th International Workshop on Content-Based Multimedia Indexing (CBMI)

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This paper describes a parallel implementation of a promising similarity search algorithm for an audio fingerprinting system. Efficient parallel implementation on a GPU accelerates the search on a dataset containing over 61 million audio fingerprints. The similarity between two fingerprints is defined as the intersection of their elements. We evaluate GPU implementations of two intersection algorithms for this dataset. We show that intelligent use of the GPU memory spaces (shared memory in particular) that maximizes the number of concurrent threads has a significant impact on the overall compute time when using fingerprints of varying dimensions. With simple modifications we obtain up to 4 times better GPU performance when using GPU memory to maximize concurrent threads. Compared to the CPU only implementations, the proposed GPU implementation reduces run times by up to 150 times for one intersection algorithm and by up to 379 times for the other intersection algorithm.

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Section: Introductionmentioning

confidence: 99%

GPU implementation of an audio fingerprints similarity search algorithm

Ouali

Dumouchel

Gupta

2015

2015 13th International Workshop on Content-Based Multimedia Indexing (CBMI)

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“…In [36], Datta et al use indexing and matching score calculation to expand textual queries on both image search and the corresponding text retrieval. By developing indexing and searching algorithms for Metric Permutation [37]. To support efficient CBIR in large image sets, Amato [38] makes use of Deep Convolutional Neural Networks.…”

Section: Introduction and Related Workmentioning

confidence: 99%

“…al. achieves efficient retrieval in large-scale multimedia databases [37]. To support efficient CBIR in large image sets, Amato [38] makes use of Deep Convolutional Neural Networks.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Analysis of Evolutionary Behavior in Self-Learning Media Search Engines

Kuang

Clement

2019

2019 IEEE International Conference on Big Data (Big Data)

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The diversity of intrinsic qualities of multimedia entities tends to impede their effective retrieval. In a Self-Learning Search Engine architecture, the subtle nuances of human perceptions and deep knowledge are taught and captured through unsupervised reinforcement learning, where the degree of reinforcement may be suitably calibrated. Such architectural paradigm enables indexes to evolve naturally while accommodating the dynamic changes of user interests. It operates by continuously constructing indexes over time, while injecting progressive improvement in search performance. For search operations to be effective, convergence of index learning is of crucial importance to ensure efficiency and robustness. In this paper, we develop a Self-Learning Search Engine architecture based on reinforcement learning using a Markov Decision Process framework. The balance between exploration and exploitation is achieved through evolutionary exploration Strategies. The evolutionary index learning behavior is then studied and formulated using stochastic analysis. Experimental results are presented which corroborate the steady convergence of the index evolution mechanism.

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