CoPhIR: a Test Collection for Content-Based Image Retrieval

By Paolo Bolettieri, Andrea Esuli, Fabrizio Falchi, Claudio Lucchese, Raffaele Perego, Tommaso Piccioli, Fausto Rabitti. 2009

178 citations recorded by the source corpus.

The scalability, as well as the effectiveness, of the different Content-based Image Retrieval (CBIR) approaches proposed in literature, is today an important research issue. Given the wealth of images on the Web, CBIR systems must in fact leap towards Web-scale datasets. In this paper, we report on our experience in building a test collection of 100 million images, with the corresponding descriptive features, to be used in experimenting new scalable techniques for similarity searching, and comparing their results. In the context of the SAPIR (Search on Audio-visual content using Peer-to-peer Information Retrieval) European project, we had to experiment our distributed similarity searching technology on a realistic data set. Therefore, since no large-scale collection was available for research purposes, we had to tackle the non-trivial process of image crawling and descriptive feature extraction (we used five MPEG-7 features) using the European EGEE computer GRID. The result of this effort is CoPhIR, the first CBIR test collection of such scale. CoPhIR is now open to the research community for experiments and comparisons, and access to the collection was already granted to more than 50 research groups worldwide.

Subfields: Computer Vision and Pattern Recognition, Information Retrieval & Search

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Citations in the corpus

Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs (2016, methodology): “CoPhIR MPEG-7 features extracted from the images [53] 2M 272 370 ms L2”
Succinct nearest neighbor search (2011, background): “Vectors are a linear combination of five different MPEG7 features [5].”
Near neighbor searching with K nearest references (2015, methodology): “CoPhIR:This is a 10-million-object subset of the CoPhIR database [11].”
Permutation Search Methods are Efficient, Yet Faster Search is Possible (2015, methodology): “At query time, the method aims to retrieve γ candidates by ﬁnding permutations that share a preﬁx of a given length with the permutation of the query object.”
Comparative Analysis of Data Structures for Approximate Nearest Neighbor Search (2014, methodology): “1) CoPhIR (L2): data set is the collection of 208-dimensional vectors extracted from images in MPEG7 format [27].”
LifeLogging: Personal Big Data (2014, background): “There are also digital photo collections, Bolettieri et al. (2009), and even email collections, Klimt and Yang (2004), or spoken conversation collections, such as the Apollo collection of six months of speech transcripts and annotations from the Apollo programme, as described in Oard and Malionek (2”
Distance based similarity models for content based multimedia retrieval (2013, background): “4 on the synthetic Clouds and CoPhIR [Bolettieri et al., 2009] databases.”