Combination of Salient Object Detection and Image Matching for Object Instance Recognition

Abstract

Object Instance Recognition aims to classify objects specifically and usually use a single reference image. It is possible to be used in many applications such as visual search, information retrieval and augmented reality. However, various things affect the appearance of the objects, which makes the recognition process harder, especially if a single reference image is used. In this paper, we proposed a combination method between Salient Object Detection and Object Instance Recognition using Image Matching and Geometric Verification. Salient Object Detection is used during initial processing (feature extraction), while Geometric Verification is performed using Best Score Increasing Subsequence (BSIS). Experimental results showed that the Fβ score and Mean Absolute Error (MAE) of saliency map on Stanford Mobile Visual Search Dataset (SMVS) are quite satisfactory. While the results of the combination method show 1.92% performance improvement than the previous method which is BSIS without Salient Object Detection.