Learning salient visual word for scalable mobile image retrieval

Yang, Xinyu; Qian, Xueming; Mei, Tao

doi:10.1016/j.patcog.2014.12.017

Cited by 34 publications

(16 citation statements)

References 47 publications

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“…Thus, many improved approaches are proposed to enhance the discrimination, e.g. visual synonyms [7], [34], [39], [40], [55], [56], embed geometry constraint [1], [8], [36], [55], [56], etc. The visual synonym can be acquired based on geometric coherence estimation.…”

Section: A Content Based Image Retrievalmentioning

confidence: 99%

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Image Location Estimation by Salient Region Matching

Qian

Zhao

Han

2015

IEEE Trans. on Image Process.

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Nowadays, locations of images have been widely used in many application scenarios for large geo-tagged image corpora. As to images which are not geographically tagged, we estimate their locations with the help of the large geo-tagged image set by content-based image retrieval. In this paper, we exploit spatial information of useful visual words to improve image location estimation (or content-based image retrieval performances). We proposed to generate visual word groups by mean-shift clustering. To improve the retrieval performance, spatial constraint is utilized to code the relative position of visual words. We proposed to generate a position descriptor for each visual word and build fast indexing structure for visual word groups. Experiments show the effectiveness of our proposed approach.

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Section: A Content Based Image Retrievalmentioning

confidence: 99%

“…Yang et al proposed to explore the contextual saliency information that mined from extended queries to improve image retrieval performances [55], [56]. They further rank the saliency for scalable mobile image retrieval with geometric consistency checking.…”

Section: A Content Based Image Retrievalmentioning

confidence: 99%

Image Location Estimation by Salient Region Matching

Qian

Zhao

Han

2015

IEEE Trans. on Image Process.

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“…These deep learning approaches learn the spatial contexts at higher levels through the models themselves to achieve enhanced generalization capabilities. The convolutional neural network (CNN), as a well-established deep learning method, has produced state-of-the-art results for multiple domains, such as visual recognition [16], image retrieval [17] and scene annotation [18]. CNNs have been introduced and actively investigated in the field of remote sensing over the past few years, focusing primarily on object detection [19] and scene classification [20].…”

Section: Introductionmentioning

confidence: 99%

VPRS-Based Regional Decision Fusion of CNN and MRF Classifications for Very Fine Resolution Remotely Sensed Images

Zhang

Sargent

Pan

et al. 2018

IEEE Trans. Geosci. Remote Sensing

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“…However, feature matching based approaches is both low accuracy and has a heavy computational cost for a large scale dataset. To solve the problem, bag-of-word (BoW) model based approaches are proposed [31][32][33][34][35][36][37][38]. In these approaches, fast image retrieval is achieved by giving each BoW a weight and then computing scores for each image in the dataset.…”

Section: Introductionmentioning

confidence: 99%

“…Our goal is to extract salient features shared by visually similar images of the same places, and utilize them to improve image location estimation performances. Han et al [42] propose a framework for saliency detection by first modeling the background and then separating salient objects from the background [36].…”

Section: Introductionmentioning

confidence: 99%