Big Data For Lifelog Moments Retrieval Improvement
The ImageCLEF lifelog Moment Retrieval Task promotes research for lifelogging retrieval by providing a benchmark with an evaluation process that allows a comparative analysis of lifelog methods, approaches and tools. In this paper, we describe our participation at the ImageCLEF lifelog LMRT 2019. Findings from our initial experiments in the LMRT sub task in ImageCLEFlifelog2018 have motivated us to improve our deep learning based processing for lifelog image retrieval approach using NoSQL database. The new version employs a distributed database and framework for storing and processing large volumes of data. We try to reduce user involvement during the ne tuning phase by using the ground truth for the development dataset. We implement our architecture using Matlab, Cassandra, and Spark. The best results were given by the rst run with precision@10=0.28. This run is based on ne tuning Googlenet with the weights freeze of the 110 rst layers.
CLEF