Performance rise in novel content based video retrieval using Vector Quantization

Pardhi, Dipak R.; Neve, Jitesh R.

doi:10.1109/iceeot.2016.7754909

Cited by 2 publications

(1 citation statement)

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“…In the relatively young and challenging field of Music Information Retrieval (MIR), the wide adoption of practical musical recommendation systems induces a research shift to contextual feature categories [16]- [18], which could be associated with audio-based techniques for enhancement [19], [20]. On the other hand, in the more sophisticated Video Retrieval where visual attributes, audio features, and narration text content are coupled, with standard protocol disappointingly absent [21], [22], partly due to the lack of high-quality training dataset and supporting information for queries [23]. Popular techniques such as deep learning and agents networks are often deployed to effect improvements in performance [24]- [27], and existing methods sometimes involve fusing distinct categories of information together through feature learning [28].…”

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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