The MIR Flickr Retrieval Evaluation Proposal Based on User Tags and Textual Passwords

In most well known image retrieval test sets, the imagery typically cannot be freely distributed or is not representative of a large community of users. In this paper we present a collection for the MIR community comprising 69,000 images from the Flickr website which are redistributable for research purposes and represent a real community of users both in the image content and image tags. We have extracted the tags and EXIF image meta data, and also make all of these publicly available. In addition we discuss several challenges for benchmarking retrieval and classification methods and applications

Image Labeling on a Network: Using Social-Network Metadata for Image Classification

Large-scale image retrieval benchmarks invariably consist of images from the Web. Many of these benchmarks are derived from online photo sharing networks, like Flickr, which in addition to hosting images also provide a highly interactive social community. Such communities generate rich metadata that can naturally be harnessed for image classification and retrieval. Here we study four popular benchmark datasets, extending them with social-network metadata, such as the groups to which each image belongs, the comment thread associated with the image, who uploaded it, their location, and their network of friends. Since these types of data are inherently relational, we propose a model that explicitly accounts for the interdependencies between images sharing common properties. We model the task as a binary labeling problem on a network, and use structured learning techniques to learn model parameters. We find that social-network metadata are useful in a variety of classification tasks, in many cases outperforming methods based on image content.Comment: ECCV 2012; 14 pages, 4 figure

Processing Large Amounts of Images on Hadoop with OpenCV

Modern image collections cannot be processed efficiently on one computer due to large collection sizes and high computational costs of modern image processing algorithms. Hence, image processing often requires distributed computing. However, distributed computing is a complicated subject that demands deep technical knowledge and often cannot be used by researches who develop image processing algorithms. The framework is needed that allows the researches to concentrate on image processing tasks and hides from them the complicated details of distributed computing. In addition, the framework should provide the researches with the familiar image processing tools. The paper describes the extension to the MapReduce Image Processing (MIPr) framework that provides the ability to use OpenCV in Hadoop cluster for distributed image processing. The modified MIPr framework allows the development of image processing programs in Java using the OpenCV Java binding. The performance testing of created system on the cloud cluster demonstrated near-linear scalability

Efficient On-the-fly Category Retrieval using ConvNets and GPUs

We investigate the gains in precision and speed, that can be obtained by using Convolutional Networks (ConvNets) for on-the-fly retrieval - where classifiers are learnt at run time for a textual query from downloaded images, and used to rank large image or video datasets. We make three contributions: (i) we present an evaluation of state-of-the-art image representations for object category retrieval over standard benchmark datasets containing 1M+ images; (ii) we show that ConvNets can be used to obtain features which are incredibly performant, and yet much lower dimensional than previous state-of-the-art image representations, and that their dimensionality can be reduced further without loss in performance by compression using product quantization or binarization. Consequently, features with the state-of-the-art performance on large-scale datasets of millions of images can fit in the memory of even a commodity GPU card; (iii) we show that an SVM classifier can be learnt within a ConvNet framework on a GPU in parallel with downloading the new training images, allowing for a continuous refinement of the model as more images become available, and simultaneous training and ranking. The outcome is an on-the-fly system that significantly outperforms its predecessors in terms of: precision of retrieval, memory requirements, and speed, facilitating accurate on-the-fly learning and ranking in under a second on a single GPU.Comment: Published in proceedings of ACCV 201

Identification of MIR-Flickr near-duplicate images : a benchmark collection for near-duplicate detection

There are many contexts where the automated detection of near-duplicate images is important, for example the detection of copyright infringement or images of child abuse. There are many published methods for the detection of similar and near-duplicate images; however it is still uncommon for methods to be objectively compared with each other, probably because of a lack of any good framework in which to do so. Published sets of near-duplicate images exist, but are typically small, specialist, or generated. Here, we give a new test set based on a large, serendipitously selected collection of high quality images. Having observed that the MIR- Flickr 1M image set contains a significant number of near-duplicate images, we have discovered the majority of these. We disclose a set of 1,958 near-duplicate clusters from within the set, and show that this is very likely to contain almost all of the near-duplicate pairs that exist. The main contribution of this publication is the identification of these images, which may then be used by other authors to make comparisons as they see fit. In particular however, near-duplicate classification functions may now be accurately tested for sensitivity and specificity over a general collection of images

Efficient Nearest Neighbors Search for Large-Scale Landmark Recognition

The problem of landmark recognition has achieved excellent results in small-scale datasets. When dealing with large-scale retrieval, issues that were irrelevant with small amount of data, quickly become fundamental for an efficient retrieval phase. In particular, computational time needs to be kept as low as possible, whilst the retrieval accuracy has to be preserved as much as possible. In this paper we propose a novel multi-index hashing method called Bag of Indexes (BoI) for Approximate Nearest Neighbors (ANN) search. It allows to drastically reduce the query time and outperforms the accuracy results compared to the state-of-the-art methods for large-scale landmark recognition. It has been demonstrated that this family of algorithms can be applied on different embedding techniques like VLAD and R-MAC obtaining excellent results in very short times on different public datasets: Holidays+Flickr1M, Oxford105k and Paris106k