• Meghna Kanaujia
• Prof. Suresh S. Gawande

Because of article discovery's cozy relationship with video examination and picture understanding, it has drawn in much exploration consideration lately. Conventional item discovery techniques are based on carefully assembled elements and shallow teachable models. Their presentation effectively deteriorates by building complex groups which consolidate different low-level picture highlights with significant level setting from object identifiers and scene classifiers. With the quick advancement in profound learning, all the more integral assets, which can learn semantic, undeniable level, further highlights, are acquainted with address the issues existing in customary designs. In this paper, an approach for object recognition based on wavelet transform is presented. This approach decomposes the input image into sub-bands by using the multiresolution analysis, Discrete Wavelet Transform (DWT). As each sub-band in the decomposed image contains useful information about the object, the mean of each sub-band is considered as features. This approach is tested on Object Image database. All the objects are considered for the classification based on one machine learning approach and calculate MSE and PSNR.

MSE, PSNR, Machine Learning, Discrete Wavelet Transform

"Performance Analysis of Object Recognition using Wavelet Transform and Machine Learning Approaches", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 1, page no.f555-f560, January-2023, Available :http://www.jetir.org/papers/JETIR2301574.pdf

"Performance Analysis of Object Recognition using Wavelet Transform and Machine Learning Approaches", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 1, page no. ppf555-f560, January-2023, Available at : http://www.jetir.org/papers/JETIR2301574.pdf