Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Model-Based Hand Tracking Using a Hierarchical Bayesian Filter
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scalable search-based image annotation of personal images
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Rapid Biologically-Inspired Scene Classification Using Features Shared with Visual Attention
IEEE Transactions on Pattern Analysis and Machine Intelligence
80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Locality sensitive hashing: A comparison of hash function types and querying mechanisms
Pattern Recognition Letters
MindFinder: interactive sketch-based image search on millions of images
Proceedings of the international conference on Multimedia
Sketch-Based Image Retrieval: Benchmark and Bag-of-Features Descriptors
IEEE Transactions on Visualization and Computer Graphics
ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
Edgel index for large-scale sketch-based image search
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Query-adaptive shape topic mining for hand-drawn sketch recognition
Proceedings of the 20th ACM international conference on Multimedia
Sketch-based image retrieval on mobile devices using compact hash bits
Proceedings of the 20th ACM international conference on Multimedia
Hi-index | 0.00 |
Because of the popularity of touch-screen devices, it has become a highly desirable feature to retrieve images from a huge repository by matching with a hand-drawn sketch. Although searching images via keywords or an example image has been successfully launched in some commercial search engines of billions of images, it is still very challenging for both academia and industry to develop a sketch-based image retrieval system on a billion-level database. In this work, we systematically study this problem and try to build a system to support query-by-sketch for two billion images. The raw edge pixel and Chamfer matching are selected as the basic representation and matching in this system, owning to the superior performance compared with other methods in extensive experiments. To get a more compact feature and a faster matching, a vector-like Chamfer feature pair is introduced, based on which the complex matching is reformulated as the crossover dot-product of feature pairs. Based on this new formulation, a compact shape code is developed to represent each image/sketch by projecting the Chamfer features to a linear subspace followed by a non-linear source coding. Finally, the multi-probe Kmedoids-LSH is leveraged to index database images, and the compact shape codes are further used for fast reranking. Extensive experiments show the effectiveness of the proposed features and algorithms in building such a sketch-based image search system.