A Fuzzy Logic Based Soft Computing Approach in CBIR System Using Incremental Filtering Feature Selection to Identify Patterns

摘要

Content based Image Retrieval (CBIR) may be a set of techniques for retrieving semantically-relevant pictures from an image database supported automatically-derived image options. Generally, in CBIR systems, the visual features are described at low-level. They are simply rigid mathematical measures that cannot influence the inherent subjectivity and fogginess of individual's understandings and perceptions. As a result, there is a niche between low-level features and high-level semantics. We have a tendency to are witnessing the era of massive information computing where computing the resources is turning into the most bottleneck to handle those massive datasets. With in the case of high dimensional data where every view of information is of high spatiality, feature selection is important for additional rising the clustering and classification results. In this paper, we have a tendency to propose a new feature selection method is Incremental Filtering Feature Selection (IFFS) algorithm that employs the Fuzzy Rough Set for choosing best subset of features and for effective grouping of huge volumes of data, respectively. We introduce a new system of visual features extraction and matching by using Fuzzy Logic (FL). FL is a powerful tool that deals with reasoning algorithms used to emulate human thinking and decision making in machines. An in depth experimental comparison of the proposed method and other methods are done. The performance of the proposed model yields promising results on the feature selection, and retrieval accuracy in the field of Content based Image Retrieval.