With the wide use of visual sensors in Internet Things (IoT) past decades, huge amounts images are captured people's daily lives, which poses challenges to traditional deep-learning-based image retrieval frameworks. Most such frameworks need a large amount annotated training data, expensive. Moreover, machines still lack human intelligence, as illustrated by fact that they pay less attention in...

Image classification is perhaps the most important part of digital image analysis. Retrieval patternbased learning is the most effective that aim to establish the relationship between the current and previous query sessions by analyzing image retrieval patterns. We propose a new feedback based and content based image retrieval system. Content based image retrieval from large resources has becom...

Relevance feedback is effective technique for bridging the semantic gap in image retrieval which diminish semantic gap between low-level visual features and high-level semantic concepts for image retrieval. Currently, crucial image retrieval system is content-based image retrieval. To improve performance of proposed content based image retrieval system, automatic relevance feedback technique is...