Deep Learning for Instance Retrieval: A Survey

Chen, Wei; Liu, Yu; Wang, Weiping; Bakker, Erwin M.; Georgiou, Theodoros; Fieguth, Paul; Liu, Li; Lew, Michael S.

doi:10.1109/tpami.2022.3218591

Cited by 77 publications

(51 citation statements)

References 126 publications

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“…To compare the image similarity from the feature vectors obtained, we can use the distance measure such as Cosine, Euclidean, Hamming, etc. ; thus based on the distance, and it can be determined top k-images which are mostly similar to the query image [1][2][3][4][5][6]. Color, texture, and shape are the primary features used in content-based image search systems.…”

Section: Related Workmentioning

confidence: 99%

“…Specifically, the image content here is represented by color, shape, texture, local features, etc..., or any information from the image query. Content-based image retrieval (CBIR) is an application of computer vision to the image search problem to retrieve related images based on a given image [1][2][3]. The system consists of an image query and an image database.…”

Section: Related Workmentioning

confidence: 99%

“…The image search system represents this sample image by the feature vector, and the distance (or similarity) between the feature vectors of the sample image and that of the image in the feature database is calculated. Finally, the system classifies the images in ascending order of distance or decreasing similarity and returns the resulting set of images to the user [1][2][3].…”

Section: Related Workmentioning

confidence: 99%

“…This method has a limitation as to how to identify and select the representative features which have highest impact on the accuracy of the search results. This selection process will take a long time in the system building process; in addition, another problem to follow is the semantic gap between the low-level feature in the form of image pixels and the high-level concept in terms of human acceptance [1][2][3][4][5][6]. In this research, we built a content-based image search system based on the multi deep neural network and K-Nearest Neighbor methodologies.…”

Section: Introductionmentioning

confidence: 99%

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Content-Based Image Retrieval using Multi Deep Neural Networks and K-Nearest Neighbor Approaches

Hùng

Pramanik

2023

Preprint

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With the rapid development of digital image technology, the number of digital images stored on the Internet environment has been increasing remarkably over the past decade. As a result, it has become a top priority to come up with an effective and convenient search tools for images. Although several image-based search tools have been introduced till now to allow users to search for images with relatively fast response times, it remains certain limitations from these tools in resolving ambiguity between content of the query image and returned image. In this research, we proposed a method exploiting multi deep neural networks and K-Nearest Neighbor methodologies in Content Based Image Retrieval to improve the image search quality. First, we built our own multi CNN models inherited from the pre-trained CNN models to extract image features. Next, basing on the method of K-Nearest Neighbors, we compiled the similarity measure of the distance between the feature vectors and conducted model experiments. Experiments were performed on the Oxford-IIIT Pet Image Dataset and self-collected dataset of a Kaggle competition. We then evaluated the model by accuracy, confusion matrix, and F1 scores; average precision (AP) and mean average precision (mAP) were used to evaluate the search results of our search proposed system. The experiments show that our proposed system achieved outstanding results.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Content-Based Image Retrieval using Multi Deep Neural Networks and K-Nearest Neighbor Approaches

Hùng

Pramanik

2023

Preprint

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“…Current RSIR methods are trained and evaluated using static RS datasets and thus is not suited for incremental scenarios [214]. Specifically, most of the RSIR methods assume that the trained model has seen all the image categories, which is however not the case in real-world applications as new RS images are constantly emerging.…”

Section: ) Incremental Learning For Rsirmentioning

confidence: 99%

Remote Sensing Image Retrieval in the Past Decade: Achievements, Challenges, and Future Directions

Zhou

Guan

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Remote sensing image retrieval (RSIR) aims to search and retrieve the images of interest from a large remote sensing (RS) image archive, which has remained to be a hot topic over the past decade. Benefited from the advent and progress of deep learning, RSIR has been promoted by developing novel approaches, constructing new datasets, and exploring potential applications. To the best of our knowledge, there lacks a comprehensive review of RSIR achievements, including systematic and hierarchical categorization of RSIR methods and benchmark datasets in the past decade. This article therefore provides a systematic survey of the recently published RSIR methods and benchmarks by reviewing more than 200 papers. To be specific, in terms of image source, label, and modality, we first group the RSIR methods into some hierarchical categories, each of which is reviewed in detail. Following the categorization of the RSIR methods, we list the benchmark datasets publically available for performance evaluation, and present our newly collected RSIR dataset. Moreover, some of the existing RSIR methods are selected and evaluated on the representative benchmark datasets. The results demonstrate that deep learning-based methods are currently the dominant RSIR approaches and outperform handcrafted feature-based methods by a significant margin. Finally, we discuss the main challenges of RSIR, and point out some potential directions for the future RSIR research.

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In‐Memory Search for Highly Efficient Image Retrieval

Ling

Zhou

et al. 2023

Advanced Intelligent Systems

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Finding similar images in real time plays a key role in information retrieval and serves as an indispensable function of the search engine. However, image retrieval involves massive distance computation. With the increase in image data volume and dimension, distance computation is suffering from huge power consumption and high computational complexity. Despite the remarkable advantages in energy efficiency shown by nonvolatile content addressable memory (nvCAM)‐based in‐memory search, achieving software‐comparable search accuracy remains a critical challenge under the impact of device variations and other nonideal factors. Here, a heterogeneous image retrieval system combining highly parallel in‐memory search with a high‐precision digital system is reported. Hamming distance (HD) can be calculated in situ with a few memory read operations on the memristor‐based CAM, and several similar images are fetched for further high‐precision rerank in the digital system. This heterogeneous computing system shows high energy efficiency (50×) compared to the CPU and higher search accuracy than the fully in‐memory computing method, thus alleviating the efficiency bottleneck of CPU‐based image retrieval.

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Deep Learning for Instance Retrieval: A Survey

Cited by 77 publications

References 126 publications

Content-Based Image Retrieval using Multi Deep Neural Networks and K-Nearest Neighbor Approaches

Content-Based Image Retrieval using Multi Deep Neural Networks and K-Nearest Neighbor Approaches

Remote Sensing Image Retrieval in the Past Decade: Achievements, Challenges, and Future Directions

In‐Memory Search for Highly Efficient Image Retrieval

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