A Study of Distance Metrics in Histogram Based Image Retrieval

Abstract

There has been a profound expansion of digital data both in terms of quality and heterogeneity. Trivial searching techniques of images by using metadata, keywords or tags are not sufficient. Efficient Content-based Image Retrieval (CBIR) is certainly the only solution to this problem. Difference between colors of two images can be an important metric to measure their similarity or dissimilarity. Content-based Image Retrieval is all about generating signatures of images in database and comparing the signature of the query image with these stored signatures. Color histogram can be used as signature of an image and used to compare two images based on certain distance metric.

What Do Different Evaluation Metrics Tell Us About Saliency Models?

“…Images are often represented as histograms or distributions of features, including low-level features like edges (texture), shape and color, and higher-level features like objects, object parts, and bags of lowlevel features. Similarity metrics applied to these feature representations have been used for classification, image retrieval, and image matching tasks [70], [74], [75]. Properties of these metrics across different computer vision tasks also apply to the task of saliency modeling, and we provide a discussion of some applications in Sec.…”