Abstract:
The pooling step is one of the key components of the well-known Bag-of-visual words (BoW) model widely used in image classification. In this paper, we propose a novel poo...Show MoreMetadata
Abstract:
The pooling step is one of the key components of the well-known Bag-of-visual words (BoW) model widely used in image classification. In this paper, we propose a novel pooling method, which is called Soft-Assignment Location-Orientation Pooling (SALOP). Inspired by the bag of statistical sampling analysis (Bossa), SALOP also explores the effect of dictionary for pooling method, but leverages both location and orientation information between the local descriptors and the atoms of dictionary to aggregate feature codes. Moreover, different from existing pooling methods, SALOP employs a soft-assignment pooling scheme to handle ambiguity and uncertainty existing in the pooling process. The evaluation is conducted on two image benchmarks: Scene15 and PASCAL VOC 2007. The experimental results show our SALOP can achieve promising performances.
Date of Conference: 27-30 September 2015
Date Added to IEEE Xplore: 10 December 2015
ISBN Information: