Bijective Weighted Kernel with Connected Component Analysis for Visual Object Search

Abstract

This letter proposes a new Bijective Weighted Kernel (BWK) with Connected Component Analysis (CCA) for visual object search. Existing match kernels often employ Term Frequency-Inverse Document Frequency (TF-IDF) weighting which is based on the occurrence frequency of visual words. As opposed to the TF-IDF, the proposed bijective match kernel is designed to exploit the Scalable Vocabulary Tree (SVT) traversal paths to weigh the quantized visual words in image matching. The BWK exploits the corresponding paths between each word in the query and database image to achieve better retrieval performance. The proposed method develops a connected component analysis to detect multiple occurrences of an object with different scales in an image. The method can reduce the computational complexity of geometric verification while achieving accurate object localization. The proposed method is evaluated on the BelgaLogos dataset . Experimental results show that the proposed method outperforms the state-of-the-art methods by a mean Average Precision (mAP) of up to 10%.