Investigating the Behavior of Compact Composite Descriptors in Early Fusion, Late Fusion and Distributed Image Retrieval

In Content-Based Image Retrieval (CBIR) systems, the visual content of the images is mapped into a new space named the feature space. The features that are chosen must be discriminative and sufficient for the description of the objects. The key to attaining a successful retrieval system is to choose the right features that represent the images as unique as possible. A feature is a set of characteristics of the image, such as color, texture, and shape. In addition, a feature can be enriched with information about the spatial distribution of the characteristic that it describes. Evaluation of the performance of low-level features is usually done on homogenous benchmarking databases with a limited number of images. In real-world image retrieval systems, databases have a much larger scale and may be heterogeneous. This paper investigates the behavior of Compact Composite Descriptors (CCDs) on heterogeneous databases of a larger scale. Early and late fusion techniques are tested and their performance in distributed image retrieval is calculated. This study demonstrates that, even if it is not possible to overcome the semantic gap in image retrieval by feature similarity, it is still possible to increase the retrieval effectiveness

Fuzzy cognitive modeling: Theoretical and practical considerations

Capítulo del libro "Czarnowski I., Howlett R., Jain L. (eds) Intelligent Decision Technologies 2019. Smart Innovation, Systems and Technologies, vol 142. Springer, Singapore"Fuzzy cognitive maps (FCMs) are knowledge-based neural systems comprised of causal relations and well-defined neural concepts Since their inception three decades ago, FCMs have been used to model a myriad of problems Despite the research progress achieved in this field, FCMs are still surrounded by important misconceptions that hamper their competitiveness in several scenarios In this paper, we discuss some theoretical and practical issues to be taken into account when modeling FCM-based systems Such issues range from the causality fallacy and the timing component to limited prediction horizon imposed by the network structure The conclusion of this paper is that the FCM’s theoretical underpinnings need to be revamped in order to overcome these limitations Closing the gap between FCMs and other neural network models seems to be the right path in that journey