This chapter approaches the image retrieval system on the base of the colors of image. It creates fuzzy signature to describe the color of image on color space HSV and builds fuzzy Hamming distance (FHD) to evaluate the similarity between the images. In order to reduce the storage space and speed up the search of similar images, it aims to create S-tree to store fuzzy signature relies on FHD and builds image retrieval algorithm on S-tree. Then, it provides the content-based image retrieval (CBIR) and an image retrieval method on FHD and S-tree. Last but not least, based on this theory, it also presents an application and experimental assessment of the process of querying similar image on the database system over 10,000 images.
Hashing is widely applied to large-scale image retrieval due to the storage and retrieval efficiency. Existing work on deep hashing assumes that the database in the target domain is identically distributed with the training set in the source domain. This paper relaxes this assumption to a transfer retrieval setting, which allows the database and the training set to come from different but relevant domains. However, the transfer retrieval setting will introduce two technical difficulties: first, the hash model trained on the source domain cannot work well on the target domain due to the large d…
Shanghai Jiao Tong University（上海交通大学）；University of Southern California（南加州大学）；Wuhan University（武汉大学）
Deep hamming hashing has gained growing popularity in approximate nearest neighbour search for large-scale image retrieval. Until now, the deep hashing for the image retrieval community has been dominated by convolutional neural network architectures, e.g. \texttt{Resnet}\cite{he2016deep}. In this paper, inspired by the recent advancements of vision transformers, we present \textbf{Transhash}, a pure transformer-based framework for deep hashing learning. Concretely, our framework is composed of two major modules: (1) Based on \textit{Vision Transformer} (ViT), we design a siamese vision transf…
Institute of Computer Science and Technology, Peking University（北京大学计算机科学技术研究所）
Hashing methods have attracted much attention for large scale image retrieval. Some deep hashing methods have achieved promising results by taking advantage of the strong representation power of deep networks recently. However, existing deep hashing methods treat all hash bits equally. On one hand, a large number of images share the same distance to a query image due to the discrete Hamming distance, which raises a critical issue of image retrieval where fine-grained rankings are very important. On the other hand, different hash bits actually contribute to the image retrieval differently, and …
Cross-modal hashing facilitates mapping of heterogeneous multimedia data into a common Hamming space, which can beutilized for fast and flexible retrieval across different modalities. In this paper, we propose a novel cross-modal hashingarchitecture-deep neural decoder cross-modal hashing (DNDCMH), which uses a binary vector specifying the presence of certainfacial attributes as an input query to retrieve relevant face images from a database. The DNDCMH network consists of two separatecomponents: an attribute-based deep cross-modal hashing (ADCMH) module, which uses a margin (m)-based loss fun…
School of Data and Computer Science, Sun Yan-Sen University（中山大学数据与计算机科学学院）
Similarity-preserving hashing is a widely-used method for nearest neighbour search in large-scale image retrieval tasks. There has been considerable research on generating efficient image representation via the deep-network-based hashing methods. However, the issue of efficient searching in the deep representation space remains largely unsolved. To this end, we propose a simple yet efficient deep-network-based multi-index hashing method for simultaneously learning the powerful image representation and the efficient searching. To achieve these two goals, we introduce the multi-index hashing (MI…
Exact and Technological Science Center, Federal University of Recôncavo da Bahia（巴伊亚州联邦大学精确与技术科学中心）；Department of Applied Mathematics, University of Campinas（坎皮纳斯大学应用数学系）
Max-C and min-D projection autoassociative fuzzy morphological memories (max-C and min-D PAFMMs) are two layer feedforward fuzzy morphological neural networks able to implement an associative memory designed for the storage and retrieval of finite fuzzy sets or vectors on a hypercube. In this paper we address the main features of these autoassociative memories, which include unlimited absolute storage capacity, fast retrieval of stored items, few spurious memories, and an excellent tolerance to either dilative noise or erosive noise. Particular attention is given to the so-called PAFMM of Zade…
In an automated search system, similarity is a key concept in solving a human task. Indeed, human process is usually a natural categorization that underlies many natural abilities such as image recovery, language comprehension, decision making, or pattern recognition. In the image search axis, there are several ways to measure the similarity between images in an image database, to a query image. Image search by content is based on the similarity of the visual characteristics of the images. The distance function used to evaluate the similarity between images depends on the criteria of the searc…
This paper gives a rigorous analysis of trained Generalized Hamming Networks(GHN) proposed by Fan (2017) and discloses an interesting finding about GHNs, i.e., stacked convolution layers in a GHN is equivalent to a single yet wide convolution layer. The revealed equivalence, on the theoretical side, can be regarded as a constructive manifestation of the universal approximation theorem Cybenko(1989); Hornik (1991). In practice, it has profound and multi-fold implications. For network visualization, the constructed deep epitomes at each layer provide a visualization of network internal represent…
With the growth of image on the web, research on hashing which enables high-speed image retrieval has been actively studied. In recent years, various hashing methods based on deep neural networks have been proposed and achieved higher precision than the other hashing methods. In these methods, multiple losses for hash codes and the parameters of neural networks are defined. They generate hash codes that minimize the weighted sum of the losses. Therefore, an expert has to tune the weights for the losses heuristically, and the probabilistic optimality of the loss function cannot be explained. In…
Binary vector embeddings enable fast nearest neighbor retrieval in large databases of high-dimensional objects, and play an important role in many practical applications, such as image and video retrieval. We study the problem of learning binary vector embeddings under a supervised setting, also known as hashing. We propose a novel supervised hashing method based on optimizing an information-theoretic quantity: mutual information. We show that optimizing mutual information can reduce ambiguity in the induced neighborhood structure in the learned Hamming space, which is essential in obtaining h…
Hashing, or learning binary embeddings of data, is frequently used in nearest neighbor retrieval. In this paper, we develop learning to rank formulations for hashing, aimed at directly optimizing ranking-based evaluation metrics such as Average Precision (AP) and Normalized Discounted Cumulative Gain (NDCG). We first observe that the integer-valued Hamming distance often leads to tied rankings, and propose to use tie-aware versions of AP and NDCG to evaluate hashing for retrieval. Then, to optimize tie-aware ranking metrics, we derive their continuous relaxations, and perform gradient-based op…
Reliable and efficient Visual Place Recognition is a major building block of modern SLAM systems. Leveraging on our prior work, in this paper we present a Hamming Distance embedding Binary Search Tree (HBST) approach for binary Descriptor Matching and Image Retrieval. HBST allows for descriptor Search and Insertion in logarithmic time by exploiting particular properties of binary Feature descriptors. We support the idea behind our search structure with a thorough analysis on the exploited descriptor properties and their effects on completeness and complexity of search and insertion. To validat…
Biometric technologies, especially face recognition, have become an essential part of identity management systems worldwide. In deployments of biometrics, secure storage of biometric information is necessary in order to protect the users' privacy. In this context, biometric cryptosystems are designed to meet key requirements of biometric information protection enabling a privacy-preserving storage and comparison of biometric data. This work investigates the application of a well-known biometric cryptosystem, i.e. the improved fuzzy vault scheme, to facial feature vectors extracted through deep…
Hashing is an efficient method for nearest neighbor search in large-scale data space by embedding high-dimensional feature descriptors into a similarity preserving Hamming space with a low dimension. However, large-scale high-speed retrieval through binary code has a certain degree of reduction in retrieval accuracy compared to traditional retrieval methods. We have noticed that multi-view methods can well preserve the diverse characteristics of data. Therefore, we try to introduce the multi-view deep neural network into the hash learning field, and design an efficient and innovative retrieval…
Nanjing University of Science & Technology（南京理工大学）；University of Waterloo（滑铁卢大学）
For more than two decades, research has been performed on content-based image retrieval (CBIR). By combining Radon projections and the support vector machines (SVM), a content-based medical image retrieval method is presented in this work. The proposed approach employs the normalized Radon projections with corresponding image category labels to build an SVM classifier, and the Radon barcode database which encodes every image in a binary format is also generated simultaneously to tag all images. To retrieve similar images when a query image is given, Radon projections and the barcode of the que…
RFHNet: Relational and Frequency-Aware Hashing Network for Large-Scale Fine-Grained Food Image Retrieval
College of Computer Science and Artificial Intelligence, Ludong University（鲁东大学计算机科学与人工智能学院）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）
Seoul National University（首尔国立大学）；NAVER Vision（NAVER 视觉）
In hash-based image retrieval systems, degraded or transformed inputs usually generate different codes from the original, deteriorating the retrieval accuracy. To mitigate this issue, data augmentation can be applied during training. However, even if augmented samples of an image are similar in real feature space, the quantization can scatter them far away in Hamming space. This results in representation discrepancies that can impede training and degrade performance. In this work, we propose a novel self-distilled hashing scheme to minimize the discrepancy while exploiting the potential of aug…
The Hong Kong University of Science and Technology（香港科技大学）；The Hong Kong University of Science and Technology Shenzhen Research Institute（香港科技大学深圳研究院）
Deep hashing has been extensively utilized in massive image retrieval because of its efficiency and effectiveness. However, deep hashing models are vulnerable to adversarial examples, making it essential to develop adversarial defense methods for image retrieval. Existing solutions achieved limited defense performance because of using weak adversarial samples for training and lacking discriminative optimization objectives to learn robust features. In this paper, we present a min-max based Center-guided Adversarial Training, namely CgAT, to improve the robustness of deep hashing networks throug…
Multiple Code Hashing for Efficient Image Retrieval
National Key Laboratory for Novel Software Technology（计算机软件新技术国家重点实验室）；Collaborative Innovation Center of Novel Software Technology and Industrialization（新型软件技术与工业化协同创新中心）；Nanjing University（南京大学）
Deep hashing has been widely applied to large-scale image retrieval tasks owing to efficient computation and low storage cost by encoding high-dimensional image data into binary codes. Since binary codes do not contain as much information as float features, the essence of binary encoding is preserving the main context to guarantee retrieval quality. However, the existing hashing methods have great limitations on suppressing redundant background information and accurately encoding from Euclidean space to Hamming space by a simple sign function. In order to solve these problems, a Cross-Scale Co…
A binary descriptor indexing scheme based on Hamming distance called the Hamming tree for local shape queries is presented. A new binary clutter resistant descriptor named Quick Intersection Count Change Image (QUICCI) is also introduced. This local shape descriptor is extremely small and fast to compare. Additionally, a novel distance function called Weighted Hamming applicable to QUICCI images is proposed for retrieval applications. The effectiveness of the indexing scheme and QUICCI is demonstrated on 828 million QUICCI images derived from the SHREC2017 dataset, while the clutter resistance…
Central South University（中南大学）；Network Resources Management and Trust Evaluation Key Laboratory of Hunan Province（湖南省网络资源管理与可信评估重点实验室）；School of Computer Science（计算机学院）
Due to its fast retrieval and storage efficiency capabilities, hashing has been widely used in nearest neighbor retrieval tasks. By using deep learning based techniques, hashing can outperform non-learning based hashing technique in many applications. However, we argue that the current deep learning based hashing methods ignore some critical problems (e.g., the learned hash codes are not discriminative due to the hashing methods being unable to discover rich semantic information and the training strategy having difficulty optimizing the discrete binary codes). In this paper, we propose a novel…
Sun Yat-sen University（中山大学）；the Hong Kong Polytechnic University（香港理工大学）；Harbin Institute of Technology（哈尔滨工业大学）
Extracting informative image features and learning effective approximate hashing functions are two crucial steps in image retrieval . Conventional methods often study these two steps separately, e.g., learning hash functions from a predefined hand-crafted feature space. Meanwhile, the bit lengths of output hashing codes are preset in most previous methods, neglecting the significance level of different bits and restricting their practical flexibility. To address these issues, we propose a supervised learning framework to generate compact and bit-scalable hashing codes directly from raw images.…
Image hash algorithms generate compact binary representations that can be quickly matched by Hamming distance, thus become an efficient solution for large-scale image retrieval. This paper proposes RV-SSDH, a deep image hash algorithm that incorporates the classical VLAD (vector of locally aggregated descriptors) architecture into neural networks. Specifically, a novel neural network component is formed by coupling a random VLAD layer with a latent hash layer through a transform layer. This component can be combined with convolutional layers to realize a hash algorithm. We implement RV-SSDH as…
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；PCL Research Center of Networks and Communications, Peng Cheng Laboratory（鹏城实验室网络通信研究中心）；vivo AI Lab（vivo AI研究院）；Department of Electrical and Computer Engineering, University of Waterloo（滑铁卢大学电气与计算机工程系）
The deep hashing based retrieval method is widely adopted in large-scale image and video retrieval. However, there is little investigation on its security. In this paper, we propose a novel method, dubbed deep hashing targeted attack (DHTA), to study the targeted attack on such retrieval. Specifically, we first formulate the targeted attack as a point-to-set optimization, which minimizes the average distance between the hash code of an adversarial example and those of a set of objects with the target label. Then we design a novel component-voting scheme to obtain an anchor code as the represen…
University of East Anglia（东安格利亚大学）；Inception Institute of Artificial Intelligence (IIAI)（人工智能初创研究院）；University of Electronic Science and Technology of China（电子科技大学）
Recent studies show that large-scale sketch-based image retrieval (SBIR) can be efficiently tackled by cross-modal binary representation learning methods, where Hamming distance matching significantly speeds up the process of similarity search. Providing training and test data subjected to a fixed set of pre-defined categories, the cutting-edge SBIR and cross-modal hashing works obtain acceptable retrieval performance. However, most of the existing methods fail when the categories of query sketches have never been seen during training. In this paper, the above problem is briefed as a novel but…
University of Tennessee, Knoxville（田纳西大学诺克斯维尔分校）；Department of Electrical Engineering and Computer Science（电气工程与计算机科学系）
Learning compact representation is vital and challenging for large scale multimedia data. Cross-view/cross-modal hashing for effective binary representation learning has received significant attention with exponentially growing availability of multimedia content. Most existing cross-view hashing algorithms emphasize the similarities in individual views, which are then connected via cross-view similarities. In this work, we focus on the exploitation of the discriminative information from different views, and propose an end-to-end method to learn semantic-preserving and discriminative binary rep…
Ankara University（安卡拉大学）；Lahore College for Women University（拉合尔女子大学）；HAVELSAN Inc.（HAVELSAN公司）
Matching of binary image features is an important step in many different computer vision applications. Conventionally, an arbitrary threshold is used to identify a correct match from incorrect matches using Hamming distance which may improve or degrade the matching results for different input images. This is mainly due to the image content which is affected by the scene, lighting and imaging conditions. This paper presents a fuzzy logic based approach for brute force matching of image features to overcome this situation. The method was tested using a well-known image database with known ground…
Cross-modal Fuzzy Alignment Network for Text-Aerial Person Retrieval and A Large-scale Benchmark
State Key Laboratory of Opto-Electronic Information Acquisition and Protection Technology（光电信息采集与防护技术国家重点实验室）；School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）；School of Artificial Intelligence, Anhui University（安徽大学人工智能学院）；The University of Hong Kong（香港大学）
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；National Laboratory of Pattern Recognition（模式识别国家重点实验室）
Visual localization to compute 6DoF camera pose from a given image has wide applications such as in robotics, virtual reality, augmented reality, etc. Two kinds of descriptors are important for the visual localization. One is global descriptors that extract the whole feature from each image. The other is local descriptors that extract the local feature from each image patch usually enclosing a key point. More and more methods of the visual localization have two stages: at first to perform image retrieval by global descriptors and then from the retrieval feedback to make 2D-3D point corresponde…
Tsinghua University（清华大学）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）
As hashing becomes an increasingly appealing technique for large-scale image retrieval, multi-label hashing is also attracting more attention for the ability to exploit multi-level semantic contents. In this paper, we propose a novel deep hashing method for scalable multi-label image search. Unlike existing approaches with conventional objectives such as contrast and triplet losses, we employ a rank list, rather than pairs or triplets, to provide sufficient global supervision information for all the samples. Specifically, a new rank-consistency objective is applied to align the similarity orde…
Unsupervised Data-Efficient Cross-Modal Retrieval with Global-Neighborhood Alignment Hashing
School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）；Shien-Ming Wu School of Intelligent Engineering, South China University of Technology（华南理工大学吴贤铭智能工程学院）；College of Computer and Information Engineering, Henan Normal University（河南师范大学计算机与信息工程学院）；VinUniversity（Vin大学）
Indian Institute of Technology Kharagpur（印度理工学院卡哈拉格普尔分校）；Microsoft（微软）
The simple approach of retrieving a closest match of a query image from one in the gallery, compares an image pair using sum of absolute difference in pixel or feature space. The process is computationally expensive, ill-posed to illumination, background composition, pose variation, as well as inefficient to be deployed on gallery sets with more than 1000 elements. Hashing is a faster alternative which involves representing images in reduced dimensional simple feature spaces. Encoding images into binary hash codes enables similarity comparison in an image-pair using the Hamming distance measur…
National University of Singapore（新加坡国立大学）；Tencent AI Lab（腾讯人工智能实验室）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）
Existing data-dependent hashing methods usually learn hash functions from pairwise or triplet data relationships, which only capture the data similarity locally, and often suffer from low learning efficiency and low collision rate. In this work, we propose a new \emph{global} similarity metric, termed as \emph{central similarity}, with which the hash codes of similar data pairs are encouraged to approach a common center and those for dissimilar pairs to converge to different centers, to improve hash learning efficiency and retrieval accuracy. We principally formulate the computation of the pro…
Contrastive masked auto-encoders based self-supervised hashing for 2D image and 3D point cloud cross-modal retrieval
Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology（华中科技大学武汉光电国家研究中心）；School of Computer Science and Technology, Huazhong University of Science and Technology（华中科技大学计算机科学与技术学院）；School of Software Engineering, Huazhong University of Science and Technology（华中科技大学软件学院）；School of Computer Science and Cyber Engineering, Guangzhou University（广州大学计算机科学与网络工程学院）
University of Chinese Academy of Sciences（中国科学院大学）
Retrieving videos of a particular person with face image as a query via hashing technique has many important applications. While face images are typically represented as vectors in Euclidean space, characterizing face videos with some robust set modeling techniques (e.g. covariance matrices as exploited in this study, which reside on Riemannian manifold), has recently shown appealing advantages. This hence results in a thorny heterogeneous spaces matching problem. Moreover, hashing with handcrafted features as done in many existing works is clearly inadequate to achieve desirable performance f…
University of Electronic Science and Technology of China（电子科技大学）；The University of Queensland（昆士兰大学）
The query-by-image video retrieval (QBIVR) task has been attracting considerable research attention recently. However, most existing methods represent a video by either aggregating or projecting all its frames into a single datum point, which may easily cause severe information loss. In this paper, we propose an efficient QBIVR framework to enable an effective and efficient video search with image query. We first define a similarity-preserving distance metric between an image and its orthogonal projection in the subspace of the video, which can be equivalently transformed to a Maximum Inner Pr…
