suit identification in people’s clothing images according to machine learning by corners

The development of artificial intelligence techniques for recognizing and classifying clothing designs has become significant. among various clothing style, the suit holds particular importance due to its widespread use in both social and professional contexts. suits can be distinguished by features such as shape, color, texture, and statistical descriptors. however, texture features are often unreliable due to their high variability, and shape-based features tend to be computationally intensive and slow. in this research, we propose a method for suit identification using color and corner detection. color features are extracted using a combination of color quantization and histogram-based spatial analysis. the spatial distribution of corners is captured using the shi-tomasi corner detector. a support vector machine (svm) classifier is employed to differentiate suit images from non-suit images. the proposed method was evaluated on a dataset of 1800 images, achieving an accuracy of 88.20% using color features and 89.90% using corner features.