Local feature descriptors have been widely used in fine-grained visual object search thanks to their robustness in scale and rotation variation and cluttered background. However, the performance of such descriptors drops under severe illumination changes. In this paper, we proposed a Discriminative and Contrast Invertible (DCI) local feature descriptor. In order to increase the discriminative ability of the descriptor under illumination changes, a Laplace gradient based histogram is proposed. A robust contrast flipping estimate is proposed based on the divergence of a local region. Experiments on fine-grained object recognition and retrieval applications demonstrate the superior performance of DCI descriptor to others.
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 [1]. Experimental results show that the proposed method outperforms the state-of-the-art methods by a mean Average Precision (mAP) of up to 10%. Index Terms-Match kernel, object localization, visual search.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.