Content-based Image Retrieval using Visual Attention Point Features

Abstract

One of the challenges in the development of a content-based image indexing and retrieval application is to achieve an efficient and robust indexing scheme. Color is a fundamental image feature used in content-based image retrieval (CBIR) systems. This paper proposes a robust and effective image retrieval scheme, which is based on the weighed color histogram of visual attention points. Firstly, the fully affine invariant visual attention points are extracted from the origin color image by using the Affine-SIFT (scale-invariant feature transform) detector. Secondly, according to the color complexity measure (CCM) theory, the visual weight values for the significant visual attention points are calculated to reflect the image local variation. Then, the weighed color histogram of visual attention points is constructed. Finally, the similarity between color images is computed by using the weighed color histogram of visual attention points. Experimental results show that the proposed image retrieval is not only more accurate and efficient in retrieving the user-interested images, but also yields higher retrieval accuracy than some state-of-the-art image retrieval schemes for various test DBs.

Influences of Human Cognition and Visual Behavior on Password Strength during Picture Password Composition

“…Images can be either provided by the GUA scheme or the user can select them. Images selected by the users can be assessed by automatic processes, such as saliency detectors [42,65], entropy estimators [63], and content-based image retrieval techniques [57], to evaluate their complexity. The dataset provided by the GUA scheme could also be assessed by humans to provide a more natural identification of the attention points, for example, by using eye-tracking.…”