scholarly journals Mobile Visual Search Compression With Grassmann Manifold Embedding

2019 ◽  
Vol 29 (11) ◽  
pp. 3356-3366
Author(s):  
Zhaobin Zhang ◽  
Li Li ◽  
Zhu Li ◽  
Houqiang Li
Keyword(s):  
Visual Search ◽  
Grassmann Manifold ◽  
Manifold Embedding ◽  
Mobile Visual Search

Rate-adaptive Compact Fisher Codes for Mobile Visual Search

2014 ◽  
Vol 21 (2) ◽  
pp. 195-198 ◽  
Author(s):  
Jie Lin ◽  
Ling-Yu Duan ◽  
Yaping Huang ◽  
Siwei Luo ◽  
Tiejun Huang ◽  
...  
Keyword(s):  
Visual Search ◽  
Mobile Visual Search ◽  
Rate Adaptive

Towards low bit rate mobile visual search with multiple-channel coding

2011 ◽  
Author(s):  
Rongrong Ji ◽  
Ling-Yu Duan ◽  
Jie Chen ◽  
Hongxun Yao ◽  
Yong Rui ◽  
...  
Keyword(s):  
Visual Search ◽  
Channel Coding ◽  
Bit Rate ◽  
Multiple Channel ◽  
Low Bit Rate ◽  
Mobile Visual Search

Compact features for mobile visual search

2015 ◽  
Author(s):  
Paul Joseph Hidalgo Flores
Keyword(s):  
Visual Search ◽  
Mobile Visual Search

scholarly journals Highly Efficient Mobile Visual Search Algorithm

2018 ◽  
Vol E101.D (12) ◽  
pp. 3073-3082 ◽  
Author(s):  
Chuang ZHU ◽  
Xiao Feng HUANG ◽  
Guo Qing XIANG ◽  
Hui Hui DONG ◽  
Jia Wen SONG
Keyword(s):  
Visual Search ◽  
Search Algorithm ◽  
Highly Efficient ◽  
Mobile Visual Search

Inter-class distribution alienation and inter-domain distribution alignment based on manifold embedding for domain adaptation

2020 ◽  
Vol 39 (6) ◽  
pp. 8149-8159
Author(s):  
Ping Li ◽  
Zhiwei Ni ◽  
Xuhui Zhu ◽  
Juan Song
Keyword(s):  
Grassmann Manifold ◽  
Domain Adaptation ◽  
Risk Minimization ◽  
Target Domain ◽  
Class Distribution ◽  
Cross Domain ◽  
Manifold Embedding ◽  
Source Data ◽  
Classifier Adaptation ◽  
Structural Risk

Domain adaptation (DA) aims to train a robust predictor by transferring rich knowledge from a well-labeled source domain to annotate a newly coming target domain; however, the two domains are usually drawn from very different distributions. Most current methods either learn the common features by matching inter-domain feature distributions and training the classifier separately or align inter-domain label distributions to directly obtain an adaptive classifier based on the original features despite feature distortion. Moreover, intra-domain information may be greatly degraded during the DA process; i.e., the source data samples from different classes might grow closer. To this end, this paper proposes a novel DA approach, referred to as inter-class distribution alienation and inter-domain distribution alignment based on manifold embedding (IDAME). Specifically, IDAME commits to adapting the classifier on the Grassmann manifold by using structural risk minimization, where inter-domain feature distributions are aligned to mitigate feature distortion, and the target pseudo labels are exploited using the distances on the Grassmann manifold. During the classifier adaptation process, we simultaneously consider the inter-class distribution alienation, the inter-domain distribution alignment, and the manifold consistency. Extensive experiments validate that IDAME can outperform several comparative state-of-the-art methods on real-world cross-domain image datasets.

A mobile visual search application for content based image retrieval in the fashion domain

2012 ◽  
Author(s):  
A. Nodari ◽  
M. Ghiringhelli ◽  
A. Zamberletti ◽  
M. Vanetti ◽  
S. Albertini ◽  
...  
Keyword(s):  
Visual Search ◽  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
Mobile Visual Search

A New Mobile Visual Search System Based on the Human Visual System

2013 ◽  
Vol 461 ◽  
pp. 792-800
Author(s):  
Bo Zhao ◽  
Hong Wei Zhao ◽  
Ping Ping Liu ◽  
Gui He Qin
Keyword(s):  
Visual Search ◽  
Visual System ◽  
Human Visual System ◽  
Large Scale ◽  
Scale Invariant ◽  
Search System ◽  
Mobile Visual Search ◽  
Extraction Algorithm ◽  
Retrieval Scheme ◽  
Scale Invariant Feature

We describe a novel mobile visual search system based on the saliencymechanism and sparse coding principle of the human visual system (HVS). In the featureextraction step, we first divide an image into different regions using thesaliency extraction algorithm. Then scale-invariant feature transform (SIFT)descriptors in all regions are extracted while regional identities arepreserved based on their various saliency levels. According to the sparsecoding principle in the HVS, we adopt a local neighbor preserving Hash functionto establish the binary sparse expression of the SIFT features. In the searchingstep, the nearest neighbors matched to the hashing codes are processed accordingto different saliency levels. Matching scores of images in the database arederived from the matching of hashing codes. Subsequently, the matching scoresof all levels are weighed by degrees of saliency to obtain the initial set of results. In order to further ensure matching accuracy, we propose an optimized retrieval scheme based on global texture information. We conduct extensive experiments on an actual mobile platform in large-scale datasets by using Corel-1000. The resultsshow that the proposed method outperforms the state-of-the-art algorithms on accuracyrate, and no significant increase in the running time of the feature extractionand retrieval can be observed.

Mobile visual search using image and text features

2011 ◽  
Author(s):  
Sam S. Tsai ◽  
Huizhong Chen ◽  
David Chen ◽  
Ramakrishna Vedantham ◽  
Radek Grzeszczuk ◽  
...  
Keyword(s):  
Visual Search ◽  
Mobile Visual Search ◽  
Text Features
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