Local vector pattern with global index angles for a content‐based image retrieval system

Abstract

This article proposes a content-based image retrieval (CBIR) system that employs an informative pattern-based descriptor. Recent literature has reported the development of efficient local-pattern-based descriptors, including the local vector pattern (LVP). This article extends the LVP formulation by making it more computationally efficient and informative. In the extended LVP-based extraction process, the global index angles are determined using the mutual information between the patterns, which are obtained from a pair of indexed angles. Thus, the Proposed LVP (PLVP) no longer requires a step to identify patterns in every indexed angle found in the querying phase of the CBIR system. A CBIR system with the PLVP is developed in this article, and the system and its associated methods are tested using data from a benchmark texture database and a natural image database. A performance comparison of the PLVP and traditional patterns, such as the local binary pattern (LBP), completely modeled local binary pattern (CLBP) and local tetra pattern (LTrP), is conducted using the CBIR system. The experimental results reveal the superiority of the PLVP in terms of precision, recall, F-score and computational efficiency.

Directional dense‐trajectory‐based patterns for dynamic texture recognition

“…The basic LVP operator [47] has been originally introduced to exploit the directional information of texture image patterns in high-order derivative spaces for face recognition. It is then interested in utilising for other applications in computer vision, such as action recognition [51], image retrieval [50]. For DT description, we get involved with this operator for the first time in order to encode directional vector structures of motion points along their dense trajectories, which are extracted from a DT sequence.…”

“…In practice, four possible directions are often employed in real applications, i.e. , to enrich discriminative information of descriptors [47, 50, 51].…”