&lt;title&gt;Cluster structure evaluation of dyadic k-means for mining large image archives&lt;/title&gt;

Daschiel, H.; Datcu, Mihai

doi:10.1117/12.463151

Cited by 5 publications

(4 citation statements)

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“…And at the same time, Schroder et al proposed to exploit Gibbs-Markov random fields (GMRF) which could be used to capture spatial information to extract features [63]. Daschiel et al suggested to utilize hierarchical Bayesian model to extract feature descriptors and these features are clustered by the dyadic k-means methods [64]. With the development of general image retrieval, Shyu et al proposed a comprehensive framework defined as geospatial information retrieval and indexing system (GeoIRIS) for RSIR based on CBIR [65].…”

Section: The Development Of Rsir Taskmentioning

confidence: 99%

Global Optimal Structured Embedding Learning for Remote Sensing Image Retrieval

Gou

Xue

et al. 2020

Sensors

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A rich line of works focus on designing elegant loss functions under the deep metric learning (DML) paradigm to learn a discriminative embedding space for remote sensing image retrieval (RSIR). Essentially, such embedding space could efficiently distinguish deep feature descriptors. So far, most existing losses used in RSIR are based on triplets, which have disadvantages of local optimization, slow convergence and insufficient use of similarity structure in a mini-batch. In this paper, we present a novel DML method named as global optimal structured loss to deal with the limitation of triplet loss. To be specific, we use a softmax function rather than a hinge function in our novel loss to realize global optimization. In addition, we present a novel optimal structured loss, which globally learn an efficient deep embedding space with mined informative sample pairs to force the positive pairs within a limitation and push the negative ones far away from a given boundary. We have conducted extensive experiments on four public remote sensing datasets and the results show that the proposed global optimal structured loss with pairs mining scheme achieves the state-of-the-art performance compared with the baselines.

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Section: The Development Of Rsir Taskmentioning

confidence: 99%

Global Optimal Structured Embedding Learning for Remote Sensing Image Retrieval

Gou

Xue

et al. 2020

Sensors

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“…Then, Datcu et al [2] introduced another information based RSIR method. The retrieval results were obtained by the dyadic k-means algorithm [29] using the feature extracted by a hierarchical Bayesian model. A comprehensive RSIR system named Geospatial Information Retrieval and Indexing System (GeoIRIS) was proposed in the literature [30].…”

Section: Feature Representation Based Rsirmentioning

confidence: 99%

Unsupervised Deep Feature Learning for Remote Sensing Image Retrieval

et al. 2018

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Due to the specific characteristics and complicated contents of remote sensing (RS) images, remote sensing image retrieval (RSIR) is always an open and tough research topic in the RS community. There are two basic blocks in RSIR, including feature learning and similarity matching. In this paper, we focus on developing an effective feature learning method for RSIR. With the help of the deep learning technique, the proposed feature learning method is designed under the bag-of-words (BOW) paradigm. Thus, we name the obtained feature deep BOW (DBOW). The learning process consists of two parts, including image descriptor learning and feature construction. First, to explore the complex contents within the RS image, we extract the image descriptor in the image patch level rather than the whole image. In addition, instead of using the handcrafted feature to describe the patches, we propose the deep convolutional auto-encoder (DCAE) model to deeply learn the discriminative descriptor for the RS image. Second, the k-means algorithm is selected to generate the codebook using the obtained deep descriptors. Then, the final histogrammic DBOW features are acquired by counting the frequency of the single code word. When we get the DBOW features from the RS images, the similarities between RS images are measured using L1-norm distance. Then, the retrieval results can be acquired according to the similarity order. The encouraging experimental results counted on four public RS image archives demonstrate that our DBOW feature is effective for the RSIR task. Compared with the existing RS image features, our DBOW can achieve improved behavior on RSIR.

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“…For the estimation a conditional least squares (CLS) estimator [14] is obtained by (4) The evidence of the model, e.g., the probability of the model given the data, can be calculated by (5) where the probability of the data can be obtained via the integration (6) From the estimated parameters, we derive several features to describe the image content: the norm of the estimated parameters as the strength of the texture, the estimate of the variance as the difference between signal and model energy [12], the evidence of the model , (5), and the local mean of the estimation kernel (Fig. 2).…”

Section: A Optical Imagesmentioning

confidence: 99%

“…1. We perform the global unsupervised clustering using a dyadic means algorithm [5], which substitutes the "clouds" of primitive features by parametric data models . Although our clustering method is slightly less accurate than means, it significantly reduces the processing time.…”

Section: Unsupervised Clustering Of Primitive Image Features and mentioning

confidence: 99%

Information mining in remote sensing image archives: system concepts

Datcu

Daschiel

Pelizzari³

et al. 2003

IEEE Trans. Geosci. Remote Sensing

203

117

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In this paper, we demonstrate the concepts of a prototype of a knowledge-driven content-based information mining system produced to manage and explore large volumes of remote sensing image data. The system consists of a computationally intensive offline part and an online interface. The offline part aims at the extraction of primitive image features, their compression, and data reduction, the generation of a completely unsupervised image content-index, and the ingestion of the catalogue entry in the database management system. Then, the user's interests-semantic interpretations of the image content-are linked with Bayesian networks to the content-index. Since this calculation is only based on a few training samples, the link can be computed online, and the complete image archive can be searched for images that contain the defined cover type. Practical applications exemplified with different remote sensing datasets show the potential of the system. Index Terms-Content-based image retrieval (CBIR), image information mining, information extraction, statistical learning.

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<title>Cluster structure evaluation of dyadic k-means for mining large image archives</title>

Cited by 5 publications

References 0 publications

Global Optimal Structured Embedding Learning for Remote Sensing Image Retrieval

Global Optimal Structured Embedding Learning for Remote Sensing Image Retrieval

Unsupervised Deep Feature Learning for Remote Sensing Image Retrieval

Information mining in remote sensing image archives: system concepts

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