Content-based image retrieval at the end of the early years

TLDR

This review surveys 200 references on content‑based image retrieval, summarizing the field’s driving forces, heritage, influence, similarity, interaction, database needs, evaluation challenges, and the semantic gap. The paper outlines the computational workflow of image retrieval systems, covering image processing by color, texture, and geometry, feature extraction, similarity assessment, user interaction, and system engineering including databases, architecture, and evaluation.

Abstract

Presents a review of 200 references in content-based image retrieval. The paper starts with discussing the working conditions of content-based retrieval: patterns of use, types of pictures, the role of semantics, and the sensory gap. Subsequent sections discuss computational steps for image retrieval systems. Step one of the review is image processing for retrieval sorted by color, texture, and local geometry. Features for retrieval are discussed next, sorted by: accumulative and global features, salient points, object and shape features, signs, and structural combinations thereof. Similarity of pictures and objects in pictures is reviewed for each of the feature types, in close connection to the types and means of feedback the user of the systems is capable of giving by interaction. We briefly discuss aspects of system engineering: databases, system architecture, and evaluation. In the concluding section, we present our view on: the driving force of the field, the heritage from computer vision, the influence on computer vision, the role of similarity and of interaction, the need for databases, the problem of evaluation, and the role of the semantic gap.