Visual-Interactive k-NDN Method (VIK): A Novel Approach to Visualize and Interact with Content-Based Image Retrieval Systems Regarding Similarity and Diversity

• Main Authors: Loosli Dias, Rafael, Mpinda, Steve Ataky T., Bueno, Renato, Ribeiro, Marcela Xavier
• Format: Conference Proceeding
• Language: English
• Subjects: Content-Based Image Retrieval; Data mining; Data visualization; Diversity; Fractals; Image retrieval; Semantics; Visual Data Mining; Visualization
• ISSN: 2375-0138
• DOI: 10.1109/iV.2017.41

Digital imaging plays an important role in many human activities, such as agriculture and forest management, earth sciences, urban planning, weather forecasting, medical imaging and so on. Processing, exploring and visualizing the inconceivable volumes of such images has turned out to be progressively troublesome. The Content-Based Image Retrieval (CBIR) remains an important issue that finds potential applications, given the place that retrieving digital images similar to a user-defined specification or pattern in huge databases now occupies in the day-to-day. CBIR systems use visual information like color, shape and texture to represent images in feature vectors. In general, there is an inconsistency in the evaluation of similarity between images according to human perception and the results computed by CBIR systems, which is called Semantic Gap. One way to improve CBIR systems is by the addition of techniques to visualize and interact with CBIR regarding similarity and diversity criteria, where the user can participate more actively in the process and steer the results according to its needs. In this paper we present the Visual-Interactive k-NDN Method (ViK): a novel approach to visualize and interact with Content-Based Image Retrieval systems. This paper aims at making use of Visual Data Mining techniques applied to queries in CBIR systems, improving the interpretability of the measure of diversity, applied using fractal analysis, as well as the relevance of results according to the prior knowledge of the user. Therefore, the user takes an active role in the content-based image retrieval, guiding its result and, consequently, reducing the Semantic Gap. Additionally, a better understanding of the diversity and similarity factors involved in the query is supported by visualization and interaction techniques.