Large-scale image retrieval using similarity preserving binary codes

Image retrieval is a fundamental problem in computer vision, and has many applications. When the dataset size gets very large, retrieving images in Internet image collections becomes very challenging. The challenges come from storage, computation speed, and similarity representation. My thesis addresses learning compact similarity preserving binary codes, which represent each image by a short binary string, for fast retrieval in large image databases. I will first present an approach called Iterative Quantization to convert high-dimensional vectors to compact binary codes, which works by learning a rotation to minimize the quantization error of mapping data to the vertices of a binary Hamming cube. This approach achieves state-of-the-art accuracy for preserving neighbors in the original feature space, as well as state-of-the-art semantic precision. Second, I will extend this approach to two different scenarios in large-scale recognition and retrieval problems. The first extension is aimed at high-dimensional histogram data, such as bag-of-words features or text documents. Such vectors are typically sparse and nonnegative. I develop an algorithm that explores the special structure of such data by mapping feature vectors to binary vertices in the positive orthant, which gives improved performance. The second extension is for Fisher Vectors, which are dense descriptors having tens of thousands to millions of dimensions. I develop a novel method for converting such descriptors to compact similarity-preserving binary codes that exploits their natural matrix structure to reduce their dimensionality using compact bilinear projections instead of a single large projection matrix. This method achieves retrieval and classification accuracy comparable to that of the original descriptors and to the state-of-the-art Product Quantization approach while having orders of magnitude faster code generation time and smaller memory footprint. Finally, I present two applications of using Internet images and tags/labels to learn binary codes with label supervision, and show improved retrieval accuracy on several large Internet image datasets. First, I will present an application that performs cross-modal retrieval in the Hamming space. Then I will present an application on using supervised binary classeme representations for large-scale image retrieval.Doctor of Philosoph

Aggregated Deep Local Features for Remote Sensing Image Retrieval

Remote Sensing Image Retrieval remains a challenging topic due to the special nature of Remote Sensing Imagery. Such images contain various different semantic objects, which clearly complicates the retrieval task. In this paper, we present an image retrieval pipeline that uses attentive, local convolutional features and aggregates them using the Vector of Locally Aggregated Descriptors (VLAD) to produce a global descriptor. We study various system parameters such as the multiplicative and additive attention mechanisms and descriptor dimensionality. We propose a query expansion method that requires no external inputs. Experiments demonstrate that even without training, the local convolutional features and global representation outperform other systems. After system tuning, we can achieve state-of-the-art or competitive results. Furthermore, we observe that our query expansion method increases overall system performance by about 3%, using only the top-three retrieved images. Finally, we show how dimensionality reduction produces compact descriptors with increased retrieval performance and fast retrieval computation times, e.g. 50% faster than the current systems.Comment: Published in Remote Sensing. The first two authors have equal contributio

Particular object retrieval with integral max-pooling of CNN activations

Recently, image representation built upon Convolutional Neural Network (CNN) has been shown to provide effective descriptors for image search, outperforming pre-CNN features as short-vector representations. Yet such models are not compatible with geometry-aware re-ranking methods and still outperformed, on some particular object retrieval benchmarks, by traditional image search systems relying on precise descriptor matching, geometric re-ranking, or query expansion. This work revisits both retrieval stages, namely initial search and re-ranking, by employing the same primitive information derived from the CNN. We build compact feature vectors that encode several image regions without the need to feed multiple inputs to the network. Furthermore, we extend integral images to handle max-pooling on convolutional layer activations, allowing us to efficiently localize matching objects. The resulting bounding box is finally used for image re-ranking. As a result, this paper significantly improves existing CNN-based recognition pipeline: We report for the first time results competing with traditional methods on the challenging Oxford5k and Paris6k datasets

Embedding based on function approximation for large scale image search

The objective of this paper is to design an embedding method that maps local features describing an image (e.g. SIFT) to a higher dimensional representation useful for the image retrieval problem. First, motivated by the relationship between the linear approximation of a nonlinear function in high dimensional space and the stateof-the-art feature representation used in image retrieval, i.e., VLAD, we propose a new approach for the approximation. The embedded vectors resulted by the function approximation process are then aggregated to form a single representation for image retrieval. Second, in order to make the proposed embedding method applicable to large scale problem, we further derive its fast version in which the embedded vectors can be efficiently computed, i.e., in the closed-form. We compare the proposed embedding methods with the state of the art in the context of image search under various settings: when the images are represented by medium length vectors, short vectors, or binary vectors. The experimental results show that the proposed embedding methods outperform existing the state of the art on the standard public image retrieval benchmarks.Comment: Accepted to TPAMI 2017. The implementation and precomputed features of the proposed F-FAemb are released at the following link: http://tinyurl.com/F-FAem