Jointly sparse hashing for image retrieval

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August undefined, 2024

Nettet4. mar. 2024 · The explosive growth of multimedia data on the Internet has magnified the challenge of information retrieval. Multimedia data usually emerges in different modalities, such as image, text, video, and audio. Unsupervised cross-modal hashing techniques that support searching among multi-modal data have gained importance in large-scale … Nettet14. okt. 2024 · Most unsupervised hashing methods use similarity matrix to guide the hash code learning, e.g., SH, AGH and BGAN. With the extracted features from the images, they usually construct the similarity matrix according to some specific criterias (such as k nearest neighbors). However, in unsupervised scenario, due to the affect of …

Jointly Sparse Hashing for Image Retrieval. - Abstract - Europe PMC

Nettet1. sep. 2024 · Jointly Subspace Hashing for Medical Image Retrieval. DOI: 10.1109/ICDH51081.2024.00060. Conference: 2024 8th International Conference on … Nettet26. aug. 2024 · Perceptual image hashing methods are often applied in various objectives, such as image retrieval, finding duplicate or near-duplicate images, and … cle to shanghai china flights

Deep semantic ranking based hashing for multi-label image …

Nettet14. apr. 2024 · Recent research reveals that deep supervised hashing has made great progress. Convolutional neural networks based hashing (CNNH) [30] was one of the … Nettet16. jan. 2024 · In recent years, many face image feature extraction and dimensional reduction algorithms have been proposed for linear and nonlinear data, such as local-based graph embedding algorithms or fuzzy set algorithms. However, the aforementioned algorithms are not very effective for face images because they are always affected by … Nettet26. aug. 2024 · Perceptual image hashing methods are often applied in various objectives, such as image retrieval, finding duplicate or near-duplicate images, and finding similar images from large-scale image content.The main challenge in image hashing techniques is robust feature extraction, which generates the same or similar … cle to sjo

Perceptual hash algorithm‐based adaptive GOP selection …

Jointly Sparse Hashing for Image Retrieval Request PDF

NettetTABLE 1: Most Influential ICCV Papers (2024-04) Highlight: This paper presents a new vision Transformer, called Swin Transformer, that capably serves as a general-purpose backbone for computer vision. Highlight: In this paper, we question if self-supervised learning provides new properties to Vision Transformer (ViT) that stand out compared to ... Nettet31. aug. 2024 · Hashing has been widely investigated for large-scale image retrieval due to its search effectiveness and computation efficiency. In this work, we propose a novel Semantic Guided Hashing method ... cle to skbNettetZero-Shot Everything Sketch-Based Image Retrieval, and in Explainable Style Fengyin Lin · Mingkang Li · Da Li · Timothy Hospedales · Yi-Zhe Song · Yonggang Qi MAGVLT: Masked Generative Vision-and-Language Transformer Sungwoong Kim · Daejin Jo · Donghoon Lee · Jongmin Kim SketchXAI: A First Look at Explainability for Human … cle to seoul

"Nettet31. aug. 2024 · Hashing has been widely investigated for large-scale image retrieval due to its search effectiveness and computation efficiency. In this work, we propose a novel … " - Jointly sparse hashing for image retrieval

Jointly sparse hashing for image retrieval

Jointly Sparse Hashing for Image Retrieval. Abstract: Recently, hash learning attracts great attentions since it can obtain fast image retrieval on large-scale data sets by using a series of discriminative binary codes. The popular methods include manifold-based hashing methods, which aim to learn the binary codes by embedding the original high ... Nettet1. feb. 2024 · To address the above challenges, we develop a novel hashing method,i.e., robust and discrete matrix factorization hashing, RDMH for short. RDMH takes two …

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NettetJointly sparse hashing for image retrieval. IEEE Trans. Image Process. 27, 12 (2024), 6147 – 6158. Google Scholar Cross Ref [23] LeCun Yann, Bottou Léon, Bengio Yoshua, and Haffner Patrick. 1998. Gradient-based learning applied to document recognition. Proc. IEEE 86, 11 (1998), 2278 – 2324. Google Scholar [24] Li Jundong, Wu Liang, Dani ... NettetInformation Retrieval Research Topic ideas for MS, or Ph.D. Degree. I am sharing with you some of the research topics regarding Information Retrieval that you can choose for your research proposal for the thesis work of MS, or Ph.D. Degree. TREC-COVID: rationale and structure of an information retrieval shared task for COVID-19.

Nettet3. mar. 2024 · Lai et al. proposed a jointly sparse hashing method by using a novel jointly sparse regression model. In [ 27 ], in order to implement large-scale image retrieval, Ning et al. proposed a sparse product quantization approach to encode the high-dimensional feature vectors into sparse feature vectors. Nettet28. apr. 2024 · Due to its effectivity and efficiency, deep hashing approaches are widely used for large-scale visual search. However, it is still challenging to produce compact and discriminative hash codes for images associated with multiple semantics for two main reasons, 1) similarity constraints designed in most of the existing methods are based …

NettetAs satellite observation technology rapidly develops, the number of remote sensing (RS) images dramatically increases, and this leads RS image retrieval tasks to be more …

NettetProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2024) Law Article-Enhanced Legal Case Matching: a Causal Learning ... Sparse Word Embeddings Using l1 Regularized Online Learning. Fei Sun ... Learning Word Representations by Jointly Modeling Syntagmatic …

Nettet12. mai 2024 · 3 The Proposed SGH Method. In this section, we propose a novel weakly-supervised hashing method coupled with binary matrix factorization for social image retrieval, called Semantic Guided Hashing (SGH), which jointly explores the hash learning, the semantic information mining and the data structure discovering. blur change 違いNettet26. apr. 2024 · Multi-view hashing can well support large-scale near-duplicate video retrieval, due to its desirable advantages of mutual reinforcement of multiple features, low storage cost, and fast retrieval speed. However, there are still two limitations that impede its performance. First, existing methods only consider local structures in multiple … blur car racing game download androidNettet14. apr. 2024 · Jointly sparse hashing for image retrieval. IEEE Trans. Image Process. (2024) View more references. Cited by (21) Semantic-based conditional generative adversarial hashing with pairwise labels. 2024, Pattern Recognition. Show abstract. blur charmless man totpNettet17. sep. 2024 · Image retrieval techniques which use neural network based hash encoding may be categorized to 3 classes: Supervised, Unsupervised, and Semi … blurcharge.comNettetRecently, hash learning attracts great attentions since it can obtain fast image retrieval on large-scale datasets by using a series of discriminative binary codes. The popular … cle to sedonaNettet24. jun. 2024 · Lai Z, Chen Y, Wu J, Wong WK, Shen F (2024) Jointly sparse hashing for image retrieval. IEEE Trans Image Process 27(12):6147–6158. Article MathSciNet Google Scholar Li K, Qi GJ, Ye J, Hua KA (2016) Linear subspace ranking hashing for cross-modal retrieval. IEEE Trans Pattern Anal Mach Intell 39(9):1825–1838 cle to sea one wayNettet29. jan. 2024 · Deep hashing methods have been shown to be the most efficient approximate nearest neighbor search techniques for large-scale image retrieval. … cle to sea alaska