Scalable Image Search with Multiple Index Tables

Cai, Junjie; Liu, Qiong; Chen, Francine; Joshi, Dhiraj

doi:10.1145/2578726.2578780

Cited by 14 publications

(8 citation statements)

References 22 publications

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“…Two widely used indexing techniques in CBIR are inverted file index and hashing based indexing. An inverted index (also called “inverted file”) is the central component of many search systems [ 41 , 42 , 43 , 44 , 45 ] as it facilitates faster and more scalable querying. Inspired by the field of information retrieval (i.e., text search engine), the inverted index stores mapping of unique word IDs to the document IDs in which the words occur [ 45 ].…”

Section: Related Workmentioning

confidence: 99%

Scalable Database Indexing and Fast Image Retrieval Based on Deep Learning and Hierarchically Nested Structure Applied to Remote Sensing and Plant Biology

et al. 2019

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Digitalisation has opened a wealth of new data opportunities by revolutionizing how images are captured. Although the cost of data generation is no longer a major concern, the data management and processing have become a bottleneck. Any successful visual trait system requires automated data structuring and a data retrieval model to manage, search, and retrieve unstructured and complex image data. This paper investigates a highly scalable and computationally efficient image retrieval system for real-time content-based searching through large-scale image repositories in the domain of remote sensing and plant biology. Images are processed independently without considering any relevant context between sub-sets of images. We utilize a deep Convolutional Neural Network (CNN) model as a feature extractor to derive deep feature representations from the imaging data. In addition, we propose an effective scheme to optimize data structure that can facilitate faster querying at search time based on the hierarchically nested structure and recursive similarity measurements. A thorough series of tests were carried out for plant identification and high-resolution remote sensing data to evaluate the accuracy and the computational efficiency of the proposed approach against other content-based image retrieval (CBIR) techniques, such as the bag of visual words (BOVW) and multiple feature fusion techniques. The results demonstrate that the proposed scheme is effective and considerably faster than conventional indexing structures.

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Section: Related Workmentioning

confidence: 99%

Scalable Database Indexing and Fast Image Retrieval Based on Deep Learning and Hierarchically Nested Structure Applied to Remote Sensing and Plant Biology

et al. 2019

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“…[24] follows the same procedure in [23] and complains about the inefficiency of MIH in finding the exact nearest neighbor for each feature. While [25] only explores the use of partial binary descriptors created in MIH as direct codebook indices, and follows a traditional BOW method to measure image similarity.…”

Section: Related Workmentioning

confidence: 99%

MILD: Multi-index hashing for appearance based loop closure detection

Han

Fang

2017

2017 IEEE International Conference on Multimedia and Expo (ICME)

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Loop Closure Detection (LCD) has been proved to be extremely useful in global consistent visual Simultaneously Localization and Mapping (SLAM) and appearance-based robot relocalization. Methods exploiting binary features in bag of words representation have recently gained a lot of popularity for their efficiency, but suffer from low recall due to the inherent drawback that high dimensional binary feature descriptors lack well-defined centroids. In this paper, we propose a realtime LCD approach called MILD (Multi-Index Hashing for Loop closure Detection), in which image similarity is measured by feature matching directly to achieve high recall without introducing extra computational complexity with the aid of Multi-Index Hashing (MIH). A theoretical analysis of the approximate image similarity measurement using MIH is presented, which reveals the trade-off between efficiency and accuracy from a probabilistic perspective. Extensive comparisons with state-of-the-art LCD methods demonstrate the superiority of MILD in both efficiency and accuracy.

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“…I MAGE retrieval is an important technique for many multimedia applications, such as face retrieval [19], object retrieval [7], and landmark identification [6]. For large-scale image retrieval tasks, one of the key components is an effective indexing method for similarity search [47], [5], particularly on high-dimensional feature space [24], [48], [36], [42]. Similarity search, a.k.a., nearest neighbor (NN) search, is a fundamental problem.…”

Section: Introductionmentioning

confidence: 99%

Scalable Image Retrieval by Sparse Product Quantization

Ning

Zhu

Zhong

et al. 2017

IEEE Trans. Multimedia

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Fast Approximate Nearest Neighbor (ANN) search technique for high-dimensional feature indexing and retrieval is the crux of large-scale image retrieval. A recent promising technique is Product Quantization, which attempts to index high-dimensional image features by decomposing the feature space into a Cartesian product of low dimensional subspaces and quantizing each of them separately. Despite the promising results reported, their quantization approach follows the typical hard assignment of traditional quantization methods, which may result in large quantization errors and thus inferior search performance. Unlike the existing approaches, in this paper, we propose a novel approach called Sparse Product Quantization (SPQ) to encoding the high-dimensional feature vectors into sparse representation. We optimize the sparse representations of the feature vectors by minimizing their quantization errors, making the resulting representation is essentially close to the original data in practice. Experiments show that the proposed SPQ technique is not only able to compress data, but also an effective encoding technique. We obtain state-of-the-art results for ANN search on four public image datasets and the promising results of content-based image retrieval further validate the efficacy of our proposed method.

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Scalable Image Search with Multiple Index Tables

Cited by 14 publications

References 22 publications

Scalable Database Indexing and Fast Image Retrieval Based on Deep Learning and Hierarchically Nested Structure Applied to Remote Sensing and Plant Biology

Scalable Database Indexing and Fast Image Retrieval Based on Deep Learning and Hierarchically Nested Structure Applied to Remote Sensing and Plant Biology

MILD: Multi-index hashing for appearance based loop closure detection

Scalable Image Retrieval by Sparse Product Quantization

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