Exploration of Cognition Impact: An Experiment with Cartoon Retrieval through Indexing

Authors

  • Prem Singh M, Sharath Kumar Y H

Keywords:

CNN, MFCC, Deep Learning

Abstract

Large-scale Cartoon Image retrieval systems are able to calculate Image-to-Image similarity and accommodate differences in timing, key and tempo. Simple vector distance measure is not adequately powerful to perform Cartoon Image recognition, and expensive solutions such as dynamic time warping do not scale to millions of instances, making Cartoon Image retrieval inappropriate for commercial-scale applications. In this work, the content-based music features of Images are used as input and transformed them into vectors by using the 2D Fourier transform approach. By projecting the Images into a fusion vector of PCA and LDA, the efficient KD Tree and R Tree Indexing algorithm is used to compare the similarity of Images and retrieve the most similar Images from the large-scale database. The proposed system is not only efficient enough to perform scalable content-based music retrieval but can also develop the potential of making similar music recognition applications faster and more accurate.

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Published

31.07.2024

How to Cite

Prem Singh M. (2024). Exploration of Cognition Impact: An Experiment with Cartoon Retrieval through Indexing . International Journal of Intelligent Systems and Applications in Engineering, 12(22s), 2047 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/7470

Issue

Vol. 12 No. 22s (2024)

Section

Research Article

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