Local Feature Detectors, Descriptors, and Image Representations: A Survey

With the advances in both stable interest region detectors and robust and distinctive descriptors, local feature-based image or object retrieval has become a popular research topic. %All of the local feature-based image retrieval system involves two important processes: local feature extraction and image representation. The other key technology for image retrieval systems is image representation such as the bag-of-visual words (BoVW), Fisher vector, or Vector of Locally Aggregated Descriptors (VLAD) framework. In this paper, we review local features and image representations for image retrieval. Because many and many methods are proposed in this area, these methods are grouped into several classes and summarized. In addition, recent deep learning-based approaches for image retrieval are briefly reviewed.Comment: 20 page

Composite Quantization

This paper studies the compact coding approach to approximate nearest neighbor search. We introduce a composite quantization framework. It uses the composition of several ($M$) elements, each of which is selected from a different dictionary, to accurately approximate a $D$-dimensional vector, thus yielding accurate search, and represents the data vector by a short code composed of the indices of the selected elements in the corresponding dictionaries. Our key contribution lies in introducing a near-orthogonality constraint, which makes the search efficiency is guaranteed as the cost of the distance computation is reduced to $O(M)$ from $O(D)$ through a distance table lookup scheme. The resulting approach is called near-orthogonal composite quantization. We theoretically justify the equivalence between near-orthogonal composite quantization and minimizing an upper bound of a function formed by jointly considering the quantization error and the search cost according to a generalized triangle inequality. We empirically show the efficacy of the proposed approach over several benchmark datasets. In addition, we demonstrate the superior performances in other three applications: combination with inverted multi-index, quantizing the query for mobile search, and inner-product similarity search

De-Hashing: Server-Side Context-Aware Feature Reconstruction for Mobile Visual Search

Due to the prevalence of mobile devices, mobile search becomes a more convenient way than desktop search. Different from the traditional desktop search, mobile visual search needs more consideration for the limited resources on mobile devices (e.g., bandwidth, computing power, and memory consumption). The state-of-the-art approaches show that bag-of-words (BoW) model is robust for image and video retrieval; however, the large vocabulary tree might not be able to be loaded on the mobile device. We observe that recent works mainly focus on designing compact feature representations on mobile devices for bandwidth-limited network (e.g., 3G) and directly adopt feature matching on remote servers (cloud). However, the compact (binary) representation might fail to retrieve target objects (images, videos). Based on the hashed binary codes, we propose a de-hashing process that reconstructs BoW by leveraging the computing power of remote servers. To mitigate the information loss from binary codes, we further utilize contextual information (e.g., GPS) to reconstruct a context-aware BoW for better retrieval results. Experiment results show that the proposed method can achieve competitive retrieval accuracy as BoW while only transmitting few bits from mobile devices.Comment: Accepted for publication in IEEE Transactions on Circuits and Systems for Video Technology (TCSVT

Recent Advance in Content-based Image Retrieval: A Literature Survey

The explosive increase and ubiquitous accessibility of visual data on the Web have led to the prosperity of research activity in image search or retrieval. With the ignorance of visual content as a ranking clue, methods with text search techniques for visual retrieval may suffer inconsistency between the text words and visual content. Content-based image retrieval (CBIR), which makes use of the representation of visual content to identify relevant images, has attracted sustained attention in recent two decades. Such a problem is challenging due to the intention gap and the semantic gap problems. Numerous techniques have been developed for content-based image retrieval in the last decade. The purpose of this paper is to categorize and evaluate those algorithms proposed during the period of 2003 to 2016. We conclude with several promising directions for future research.Comment: 22 page

Learning to Index for Nearest Neighbor Search

In this study, we present a novel ranking model based on learning neighborhood relationships embedded in the index space. Given a query point, conventional approximate nearest neighbor search calculates the distances to the cluster centroids, before ranking the clusters from near to far based on the distances. The data indexed in the top-ranked clusters are retrieved and treated as the nearest neighbor candidates for the query. However, the loss of quantization between the data and cluster centroids will inevitably harm the search accuracy. To address this problem, the proposed model ranks clusters based on their nearest neighbor probabilities rather than the query-centroid distances. The nearest neighbor probabilities are estimated by employing neural networks to characterize the neighborhood relationships, i.e., the density function of nearest neighbors with respect to the query. The proposed probability-based ranking can replace the conventional distance-based ranking for finding candidate clusters, and the predicted probability can be used to determine the data quantity to be retrieved from the candidate cluster. Our experimental results demonstrated that the proposed ranking model could boost the search performance effectively in billion-scale datasets.Comment: This paper was accepted by IEEE Transcations on Pattern Analysis and Machine Intelligence in March 201

Joint Maximum Purity Forest with Application to Image Super-Resolution

In this paper, we propose a novel random-forest scheme, namely Joint Maximum Purity Forest (JMPF), for classification, clustering, and regression tasks. In the JMPF scheme, the original feature space is transformed into a compactly pre-clustered feature space, via a trained rotation matrix. The rotation matrix is obtained through an iterative quantization process, where the input data belonging to different classes are clustered to the respective vertices of the new feature space with maximum purity. In the new feature space, orthogonal hyperplanes, which are employed at the split-nodes of decision trees in random forests, can tackle the clustering problems effectively. We evaluated our proposed method on public benchmark datasets for regression and classification tasks, and experiments showed that JMPF remarkably outperforms other state-of-the-art random-forest-based approaches. Furthermore, we applied JMPF to image super-resolution, because the transformed, compact features are more discriminative to the clustering-regression scheme. Experiment results on several public benchmark datasets also showed that the JMPF-based image super-resolution scheme is consistently superior to recent state-of-the-art image super-resolution algorithms.Comment: 18 pages, 7 figure

Fine-tuning CNN Image Retrieval with No Human Annotation

Image descriptors based on activations of Convolutional Neural Networks (CNNs) have become dominant in image retrieval due to their discriminative power, compactness of representation, and search efficiency. Training of CNNs, either from scratch or fine-tuning, requires a large amount of annotated data, where a high quality of annotation is often crucial. In this work, we propose to fine-tune CNNs for image retrieval on a large collection of unordered images in a fully automated manner. Reconstructed 3D models obtained by the state-of-the-art retrieval and structure-from-motion methods guide the selection of the training data. We show that both hard-positive and hard-negative examples, selected by exploiting the geometry and the camera positions available from the 3D models, enhance the performance of particular-object retrieval. CNN descriptor whitening discriminatively learned from the same training data outperforms commonly used PCA whitening. We propose a novel trainable Generalized-Mean (GeM) pooling layer that generalizes max and average pooling and show that it boosts retrieval performance. Applying the proposed method to the VGG network achieves state-of-the-art performance on the standard benchmarks: Oxford Buildings, Paris, and Holidays datasets.Comment: TPAMI 2018. arXiv admin note: substantial text overlap with arXiv:1604.0242

Unifying Deep Local and Global Features for Image Search

Image retrieval is the problem of searching an image database for items that are similar to a query image. To address this task, two main types of image representations have been studied: global and local image features. In this work, our key contribution is to unify global and local features into a single deep model, enabling accurate retrieval with efficient feature extraction. We refer to the new model as DELG, standing for DEep Local and Global features. We leverage lessons from recent feature learning work and propose a model that combines generalized mean pooling for global features and attentive selection for local features. The entire network can be learned end-to-end by carefully balancing the gradient flow between two heads -- requiring only image-level labels. We also introduce an autoencoder-based dimensionality reduction technique for local features, which is integrated into the model, improving training efficiency and matching performance. Comprehensive experiments show that our model achieves state-of-the-art image retrieval on the Revisited Oxford and Paris datasets, and state-of-the-art single-model instance-level recognition on the Google Landmarks dataset v2. Code and models are available at https://github.com/tensorflow/models/tree/master/research/delf .Comment: ECCV'20 pape

Constrained-size Tensorflow Models for YouTube-8M Video Understanding Challenge

This paper presents our 7th place solution to the second YouTube-8M video understanding competition which challenges participates to build a constrained-size model to classify millions of YouTube videos into thousands of classes. Our final model consists of four single models aggregated into one tensorflow graph. For each single model, we use the same network architecture as in the winning solution of the first YouTube-8M video understanding competition, namely Gated NetVLAD. We train the single models separately in tensorflow's default float32 precision, then replace weights with float16 precision and ensemble them in the evaluation and inference stages., achieving 48.5% compression rate without loss of precision. Our best model achieved 88.324% GAP on private leaderboard. The code is publicly available at https://github.com/boliu61/youtube-8mComment: Accepted paper at 2018 ECCV Youtube8M workshop: https://research.google.com/youtube8m/workshop2018

Application-Driven Near-Data Processing for Similarity Search

Similarity search is a key to a variety of applications including content-based search for images and video, recommendation systems, data deduplication, natural language processing, computer vision, databases, computational biology, and computer graphics. At its core, similarity search manifests as k-nearest neighbors (kNN), a computationally simple primitive consisting of highly parallel distance calculations and a global top-k sort. However, kNN is poorly supported by today's architectures because of its high memory bandwidth requirements. This paper proposes an application-driven near-data processing accelerator for similarity search: the Similarity Search Associative Memory (SSAM). By instantiating compute units close to memory, SSAM benefits from the higher memory bandwidth and density exposed by emerging memory technologies. We evaluate the SSAM design down to layout on top of the Micron hybrid memory cube (HMC), and show that SSAM can achieve up to two orders of magnitude area-normalized throughput and energy efficiency improvement over multicore CPUs; we also show SSAM is faster and more energy efficient than competing GPUs and FPGAs. Finally, we show that SSAM is also useful for other data intensive tasks like kNN index construction, and can be generalized to semantically function as a high capacity content addressable memory.Comment: 15 pages, 8 figures, 7 table