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

3D Shape Descriptor-Based Facial Landmark Detection: A Machine Learning Approach

Facial landmark detection on 3D human faces has had numerous applications in the literature such as establishing point-to-point correspondence between 3D face models which is itself a key step for a wide range of applications like 3D face detection and authentication, matching, reconstruction, and retrieval, to name a few. Two groups of approaches, namely knowledge-driven and data-driven approaches, have been employed for facial landmarking in the literature. Knowledge-driven techniques are the traditional approaches that have been widely used to locate landmarks on human faces. In these approaches, a user with sucient knowledge and experience usually denes features to be extracted as the landmarks. Data-driven techniques, on the other hand, take advantage of machine learning algorithms to detect prominent features on 3D face models. Besides the key advantages, each category of these techniques has limitations that prevent it from generating the most reliable results. In this work we propose to combine the strengths of the two approaches to detect facial landmarks in a more ecient and precise way. The suggested approach consists of two phases. First, some salient features of the faces are extracted using expert systems. Afterwards, these points are used as the initial control points in the well-known Thin Plate Spline (TPS) technique to deform the input face towards a reference face model. Second, by exploring and utilizing multiple machine learning algorithms another group of landmarks are extracted. The data-driven landmark detection step is performed in a supervised manner providing an information-rich set of training data in which a set of local descriptors are computed and used to train the algorithm. We then, use the detected landmarks for establishing point-to-point correspondence between the 3D human faces mainly using an improved version of Iterative Closest Point (ICP) algorithms. Furthermore, we propose to use the detected landmarks for 3D face matching applications

Generative Prior for Unsupervised Image Restoration

The challenge of restoring real world low-quality images is due to a lack of appropriate training data and difficulty in determining how the image was degraded. Recently, generative models have demonstrated great potential for creating high- quality images by utilizing the rich and diverse information contained within the model’s trained weights and learned latent representations. One popular type of generative model is the generative adversarial network (GAN). Many new methods have been developed to harness the information found in GANs for image manipulation. Our proposed approach is to utilize generative models for both understanding the degradation of an image and restoring it. We propose using a combination of cycle consistency losses and self-attention to enhance face images by first learning the degradation and then using this information to train a style-based neural network. We also aim to use the latent representation to achieve a high level of magnification for face images (x64). By incorporating the weights of a pre-trained StyleGAN into a restoration network with a vision transformer layer, we hope to improve the current state-of-the-art in face image restoration. Finally, we present a projection-based image-denoising algorithm named Noise2Code in the latent space of the VQGAN model with a fixed-point regularization strategy. The fixed-point condition follows the observation that the pre-trained VQGAN affects the clean and noisy images in a drastically different way. Unlike previous projection-based image restoration in the latent space, both the denoising network and VQGAN model parameters are jointly trained, although the latter is not needed during the testing. We report experimental results to demonstrate that the proposed Noise2Code approach is conceptually simple, computationally efficient, and generalizable to real-world degradation scenarios

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