Unsupervised Distance Learning by Rank Correlation Measures for Image Retrieval

Okada, César Yugo; Pedronette, Daniel Carlos Guimarães; Torres, Ricardo da Silva

doi:10.1145/2671188.2749335

Cited by 15 publications

(11 citation statements)

References 41 publications

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“…But The Jaccard similarity does not include order information [27]. In [28], a Jaccard similarity considering different depths was presented, which gives more weight to the top ranked results than lower results. Webber et al [29] also proposed a similarity, rank-biased overlap (RBO), based on a simple model in which the user compares the similarity of the two ranking lists at incrementally increasing depths.…”

Section: A Image Similarity Measurement Approaches In Rsirmentioning

confidence: 99%

Query-Adaptive Remote Sensing Image Retrieval Based on Image Rank Similarity and Image-to-Query Class Similarity

Zhao

Luo

et al. 2020

IEEE Access

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Many image features have been proposed for image retrieval; hence, effectively fusing these features to alleviate the large variation in performance among image queries when using single image features has become a major challenge in remote sensing (RS) image retrieval. Because high-resolution remote sensing images have abundant and complex visual contents, accurately measuring the similarity between two images is another important problem. To address these challenges, we propose a novel RS image retrieval method that uses query-adaptive feature weights to fuse features and utilizes two image similarities to improve retrieval performance. First, we use the image rank similarity, which measures the similarity between two images according to their corresponding top-m image lists from a reference image collection, to calculate the similarity of each feature between a query image and each retrieved image. Then, we assign a weight to each feature to fuse these features via our query-adaptive weighting method. Finally, we take the query image and its neighborhood set selected from the retrieval dataset as the query class and utilize the image-to-query class similarity to re-rank the retrieval results. Extensive experiments are conducted on two publicly available RS image databases. Compared with the state-of-the-art methods, the proposed method can significantly enhance the retrieval precision.

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Section: A Image Similarity Measurement Approaches In Rsirmentioning

confidence: 99%

Query-Adaptive Remote Sensing Image Retrieval Based on Image Rank Similarity and Image-to-Query Class Similarity

Zhao

Luo

et al. 2020

IEEE Access

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“…Based on this observation, the RL-Sim* Algorithm [Okada et al 2015] was proposed. The RL-Sim* Algorithm [Okada et al 2015] computes a different distance when there is no overlap between top-k positions. In this way, considering a query image img i the ranked list τ i is divided in three segments.…”

Section: Rl-sim* Algorithmmentioning

confidence: 99%

“…As a result, the distance computed based on the segmented rank list presents a higher retrieval accuracy. Other relevant contribution of the RL-Sim * Algorithm [Okada et al 2015] consists in the use and evaluation of several rank correlation measures.…”

Section: Rl-sim* Algorithmmentioning

confidence: 99%

“…In this paper, we discuss the acceleration of a recent effective distance learning algorithm for image retrieval. The RL-Sim * [Okada et al 2015] algorithm exploits the similarity between ranked lists through rank correlation measures for improving the image retrieval accuracy. The objective is to accelerated the algorithm in order to allow its use in real time applications.…”

Section: Introductionmentioning

confidence: 99%

“…The experimental results demonstrated that the proposed approach improves the efficiency of the algorithm without significant losses of effectiveness. This paper is organized as follow: Section 2 briefly describes the original RL-Sim * algorithm [Okada et al 2015]. Section 3 presents the proposed accelerated RL-Sim * algorithm.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Efficient Unsupervised Distance Learning through Rank Correlation Measures on Heterogeneous Systems

Okada¹,

Pedronette²

2016

Anais Do XVII Simpósio Em Sistemas Computacionais De Alto Desempenho (SSCAD 2016)

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The huge growth of image collections have demanded methods capable of conducting effective and efﬁcient image searches. Among the most promising approaches, the Content-Based Image Retrieval (CBIR) systems have established as an alternative for automatically taking into account the visual information. Despite the important results achieved, retrieving relevant images (effectiveness) in minimal time (efﬁciency) remains a challenge task. Recently, unsupervised learning algorithms have been proposed to improve the effectiveness of CBIR systems by exploiting similarity and ranking information. Such algorithms does not require any user information, but often demand high computational efforts. On the other hand, parallel and heterogeneous approaches constitute a feasible solution for high performance computing. In this paper, we discuss a parallel and accelerated solution for computing the RL-Sim∗ Algorithm, a recently proposed unsupervised image re-ranking approach. The proposed algorithm uses the OpenCL standard, exploiting both CPU and GPU devives in an Accelerated Processing Unit (APU). The experimental evaluation demonstrated that signiﬁcant speedups were achieved when compared with the original approach.

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