Evaluation of local and global descriptors for emotional impact recognition

Abstract

In order to model the concept of emotion and to extract the emotional impact from images, one may search suitable image processing features. However, in the literature, there is no consensus on the ones to consider since they are often linked to the application. Obviously, the perception of emotion is not only influenced by the content of the images, it is also modified by some personal experiences like cultural aspects and semantic associated to some colours or objects. In this paper, we choose low level features frequently used in CBIR especially those based on SIFT descriptors. To take into account the complex process of emotion perception, we also consider colour and texture features and one global scene descriptor: GIST. We supposed the chosen features could implicitly encode high-level information about emotions due to their accuracy in the different CBIR applications of the literature. We test our methodology on two databases: SENSE and IAPS.

Improving Efficiency of CBIR by using Color, Texture and Fusion Features with Bit Pattern

“…Can be determined over the consequences of image division and edge detection algorithms. Object shape is an example of such feature [14].…”

A Survey on Different Techniques of Cbir

“…[7] present that On this paper,settle on probably of low stage features utilized as a part of CBIR particularly these focused n SIFT descriptors. To do not forget difficult emotion belief procedure, here don't forget color and texture features and one international scene descriptor: GIST.…”

A survey on cbir techniques and learning algorithm comparison

“…[14] present that On this paper, decide upon most likely of low stage facets used in CBIR particularly these centered n SIFT descriptors. To do not forget difficult emotion belief procedure, here don't forget color and texture features and one international scene descriptor: GIST.…”

“…To do not forget difficult emotion belief procedure, here don't forget color and texture features and one international scene descriptor: GIST. Supposed the select features could implicitly encrypt highlevel data about emotions because of their accuracy in the different CBIR applications [14].…”