Image selection based on low level properties for lifelog moment retrieval

Ricardo F. Ribeiro; António J. R. Neves; José Luis Oliveira

doi:10.1117/12.2557073

31 January 2020 Image selection based on low level properties for lifelog moment retrieval

Ricardo F. Ribeiro, António J. R. Neves, José Luis Oliveira

Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 1143303 (2020) https://doi.org/10.1117/12.2557073
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands

Abstract

The increasing number of mobile and wearable devices is dramatically changing the way we collect data about person’s life. These devices allow recording our daily activities and behavior in several forms, e.g., text, images, bio-signals, or video. However, many times, the collected data includes low quality or irrelevant contents, feeding lifelogging applications with huge amounts of data, and creating computational challenges for patterns’ identification. In this paper, we propose a fast image analysis approach to automatically select relevant images from lifelog data. Using images intrinsic information, such as scenes and objects, we have manually curated two datasets, one with relevant content and another with non-relevant information. Then, we applied supervised learning algorithms based on low-level image features, namely blur and focus, to find the binary model that best discriminates between the two classes. The binary models were then compared based on learning curves and f1-scores, achieving a 95.4% of f1-score for the best one. By reducing the amount of images in the lifelog data, we were able to save computational time without losing images with relevant content.

Citation Download Citation

Ricardo F. Ribeiro, António J. R. Neves, and José Luis Oliveira "Image selection based on low level properties for lifelog moment retrieval", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 1143303 (31 January 2020); https://doi.org/10.1117/12.2557073

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