The scale of picture collecting and data storage technologies is
growing, as is the enlargement of quantity and quality of the image DB.
Previously, retrieval of photos was accomplished through verbal
descriptions and manual labelling of photographs, labour-intensive
process. The necessity is for efficient systems termed content-based
image retrieval systems to manage big collections. In this situation,
the image’s visual contents, such as the shape, layout, and colour of
the items included in the picture, are taken into account, as well as
the image’s related data. These systems are more efficient and faster
than other traditional methods of picture retrieval. In this paper, a
novel method for extracting features using Gabor filtering is presented,
which is then optimised through lion optimization. Finally, SVM is
employed with cuckoo search optimization, while the decision tree
technique is used with lion search optimization. The proposed method is
put to the test on a variety of parameters, and the results reveal that
Lion optimization outperforms cuckoo search.