Feature Extraction using Convolutional Neural Network for Building Image Retrieval
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Abstract
This research aimed to extract image features using Convolutional Neural Networks (CNNs) for a content-based image retrieval (CBIR) system of buildings. A dataset of 513 photographs of various buildings within Buriram Rajabhat University was utilized. Three CNN models, VGG16, VGG19, and NasNetLarge, were employed for feature extraction, with Cosine similarity. The results indicated that VGG19 yielded the highest accuracy in image retrieval, followed by VGG16 and NasNetLarge, respectively. This suggests that VGG19 is more effective at extracting building image features than VGG16 and NasNetLarge. However, the study revealed that NasNetLarge, a more complex model, had limitations in extracting features from building images, possibly due to its higher structural complexity compared to VGG16 and VGG19, resulting in lower performance in this specific application.
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
ลิขสิทธิ์ต้นฉบับที่ได้รับการตีพิมพ์ในวารสารนวัตกรรมวิทยาศาสตร์เพื่อการพัฒนาอย่างยั่งยืนถือเป็นกรรมสิทธิ์ของคณะวิทยาศาสตร์และเทคโนโลยี มหาวิทยาลัยสวนดุสิต ห้ามผู้ใดนำข้อความทั้งหมดหรือบางส่วนไปพิมพ์ซ้ำ เว้นแต่จะได้รับอนุญาตอย่างเป็นลายลักษณ์อักษรจากคณะวิทยาศาสตร์และเทคโนโลยี มหาวิทยาลัยสวนดุสิต นอกจากนี้ เนื้อหาที่ปรากฎในบทความเป็นความรับผิดชอบของผู้เขียน ทั้งนี้ไม่รวมความผิดพลาดอันเกิดจากเทคนิคการพิมพ์
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