A new method for visual descriptor evaluation

Evaluation in visual information retrieval is usually performed by executing test queries and calculating recall and precision based on predefined media collections and ground truth information. This process is complex and time consuming. For the evaluation of feature transformations (transformation of visual media objects to feature vectors) it would be desirable to have simpler methods available. In this paper we introduce an evaluation procedure for features that is based on statistical data analysis. The new idea is that we make use of the existing visual MPEG-7 descriptors to judge the characteristics of novel feature transformations. The proposed procedure is divided into four steps: (1) feature extraction, (2) merging with MPEG-7 data and normalisation, (3) statistical data analysis and (4) visualisation and interpretation. Three types of statistical methods are used for evaluation: (1) description (moments, etc.), (2) identification of similarities (e.g. cluster analysis) and (3) identification of dependencies (e.g. factor analysis). From statistical analysis several benefits can be drawn for feature redesign. Application of the evaluation procedure suggested and advantages of the approach are shown in several examples.