Publication | Closed Access
Rate-accuracy optimization of binary descriptors
48
Citations
11
References
2013
Year
Unknown Venue
Lossy CompressionEngineeringMachine LearningFeature DetectionImage AnalysisData ScienceImage CompressionPattern RecognitionVariable-length CodeMachine VisionComputer EngineeringComputer ScienceImage SimilarityDeep LearningData CompressionComputer VisionImage CodingInternal OrderingBinary StringBinary Descriptors
Binary descriptors have recently emerged as low-complexity alternatives to state-of-the-art descriptors such as SIFT. The descriptor is represented by means of a binary string, in which each bit is the result of the pair-wise comparison of smoothed pixel values properly selected in a patch around each keypoint. Previous works have focused on the construction of the descriptor neglecting the opportunity of performing lossless compression. In this paper, we propose two contributions. First, design an entropy coding scheme that seeks the internal ordering of the descriptor that minimizes the number of bits necessary to represent it. Second, we compare different selection strategies that can be adopted to identify which pair-wise comparisons to use when building the descriptor. Unlike previous works, we evaluate the discriminative power of descriptors as a function of rate, in order to investigate the trade-offs in a bandwidth constrained scenario.
| Year | Citations | |
|---|---|---|
Page 1
Page 1