Comparative Analysis of Support Vector Machine and Convolutional Neural Network for Image Classification

The rapid growth of image classification tasks in various applications, from facial recognition to object detection, highlights the importance of developing efficient and accurate classification techniques. In particular, distinguishing between visually similar categories, such as dogs and cats, presents unique challenges due to the subtle differences in features like fur texture, facial structure, and posture. In this paper, we compare two distinct approaches for the binary classification of dog and cat images: a traditional machine learning technique and a deep learning method. The first approach involves feature extraction using Histogram of Oriented Gradients (HOG) followed by classification with a Support Vector Machine (SVM). The second approach employs a Convolutional Neural Network (CNN), a deep learning architecture known for its ability to automatically learn hierarchical features from image data. Our primary objective is to evaluate and analyze the performance of these two methods in terms of accuracy, efficiency, and overall effectiveness. Experimental results demonstrate that the CNN model significantly outperforms the traditional machine learning approach, achieving higher classification accuracy and superior feature extraction without manual intervention. These findings emphasize the growing importance of CNNs for image-based classification problems, as they demonstrate higher performance and flexibility over conventional machine learning models.