Resource-Efficient Image Retrieval: A Study of Local Patterns Versus Deep Learning Models

This research paper delves into the ongoing debate between traditional local patterns and advanced deep learning models in the context of image retrieval from less complex datasets. Through a comprehensive comparative analysis, the study examines the trade-offs and advantages of employing these approaches for retrieving images from a standard dataset. Emphasis is placed on understanding how these methods impact computational resource usage, including CPU/GPU utilization and memory consumption. Our findings reveal that deep learning models show promise in handling complex image datasets, while local patterns, known for their resource efficiency, may struggle with capturing fine-grained visual details. This research underscores the importance of considering specific requirements and resource efficiency when choosing between these methods. In an era marked by the exponential growth of image data, this study contributes to the broader discourse, encouraging continued exploration and innovation for more effective and efficient image retrieval solutions.