YFCC100M HybridNet fc6 Deep Features for Content Based Image Retrieval
This paper presents a corpus of deep features extracted from the YFCC100M images considering the fc6 hidden layer activation of the HybridNet deep convolutional neural network. For a set of random selected queries we made available k NN results obtained sequentially scanning the entire set features comparing both using the Euclidean and Hamming Distance on a binarized version of the features. This set of results is ground truth for evaluating Content Based Image Retrieval (CBIR) systems that use approximate similarity search methods for efficient and scalable indexing. Moreover, we present experimental results obtained indexing this corpus with two distinct approaches: the Metric Inverted File and the Lucene Quantization. These two CBIR systems are public available online allowing real time search using both internal and external queries.
MMCommons ACM Multimedia