Interactive object-based image retrieval and annotation on iPad

Han, Junwei; Xu, Mingxing; Li, Xin; Liu, Tianming

doi:10.1007/s11042-013-1509-6

Cited by 9 publications

(1 citation statement)

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“…Han et al propose an object-based image retrieval algorithm. They generate a feature descriptor based on context-preserving bag-of-words, and utilize a two-stage re-ranking technique to measure the similarity between the query image and each image in the dataset [27]. A mixture of multi-scale deformable part-based model is trained for each object category by training a latent support vector machine [3].…”

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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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%