Active query sensing: Suggesting the best query view for mobile visual search
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) - Special section of best papers of ACM multimedia 2011, and special section on 3D mobile multimedia
Compact kernel hashing with multiple features
Proceedings of the 20th ACM international conference on Multimedia
Listen, look, and gotcha: instant video search with mobile phones by layered audio-video indexing
Proceedings of the 21st ACM international conference on Multimedia
Order preserving hashing for approximate nearest neighbor search
Proceedings of the 21st ACM international conference on Multimedia
Towards efficient sparse coding for scalable image annotation
Proceedings of the 21st ACM international conference on Multimedia
LAVES: an instant mobile video search system based on layered audio-video indexing
Proceedings of the 21st ACM international conference on Multimedia
Saliency-Based region log covariance feature for image copy detection
IWDW'12 Proceedings of the 11th international conference on Digital Forensics and Watermaking
Hash Bit Selection Using Markov Process for Approximate Nearest Neighbor Search
Proceedings of International Conference on Advances in Mobile Computing & Multimedia
Multiple feature kernel hashing for large-scale visual search
Pattern Recognition
SalientShape: group saliency in image collections
The Visual Computer: International Journal of Computer Graphics
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Rapidly growing applications on smartphones have provided an excellent platform for mobile visual search. Most of previous visual search systems adopt the framework of ”Bag of Words”, in which words indicate quantized codes of visual features. In this work, we propose a novel visual search system based on ”Bag of Hash Bits” (BoHB), in which each local feature is encoded to a very small number of hash bits, instead of quantized to visual words, and the whole image is represented as bag of hash bits. The proposed BoHB method offers unique benefits in solving the challenges associated with mobile visual search, e.g., low transmission cost, cheap memory and computation on the mobile side, etc. Moreover, our BoHB method leverages the distinct properties of hashing bits such as multi-table indexing, multiple bucket probing, bit reuse, and hamming distance based ranking to achieve efficient search over gigantic visual databases. The proposed method significantly outperforms state-of-the-art mobile visual search methods like CHoG, and other (conventional desktop) visual search approaches like bag of words via vocabulary tree, or product quantization. The proposed BoHB approach is easy to implement on mobile devices, and general in the sense that it can be applied to different types of local features, hashing algorithms and image databases. We also incorporate a boundary feature in the reranking step to describe the object shapes, complementing the local features that are usually used to characterize the local details. The boundary feature can further filter out noisy results and improve the search performance, especially at the coarse category level. Extensive experiments over large-scale data sets up to 400k product images demonstrate the effectiveness of our approach.