| 摘要 |
<P>PROBLEM TO BE SOLVED: To raise recognition accuracy when object recognition using a local feature quantity is performed by LSH. <P>SOLUTION: A hash function is applied to a model feature quantity vector representing the feature quantity of a model feature point extracted from a model image, and the model feature quantity vector is quantized. Quantization is performed so that an inner product value between the model feature quantity vector and a hash function vector is compared with a threshold, and is quantized to one value of two values of 1 and 0. In addition, when the two values are within a quantization error margin which is set to the hash function vector on a feature quantity space, the other value of the two values is also set as a quantization value. The model feature point which has the model feature quantity vector is clustered so as to belong to a plurality of clusters based on the quantization value of the model feature quantity vector. This invention is applicable to a device which performs an object recognition using the local feature quantity. <P>COPYRIGHT: (C)2012,JPO&INPIT |
