KTRICT A KAZE Feature Extraction

Abstract

To improve the retrieval accuracy in CBIR system means reducing this semantic gap. Reducing semantic is a necessity to build a better, trusted system, since CBIR systems are applied to a lot of fields that require utmost accuracy. Time constraint is also a very important factor since a fast CBIR system leads to a fast completion of different tasks. The aim of the paper is to build a CBIR system that provides high accuracy in lower time complexity and work towards bridging the aforementioned semantic gap. CBIR systems retrieve images that are related to query image (QI) from huge datasets. The traditional CBIR systems include two phases: feature extraction and similarity matching. Here, a technique called KTRICT, a KAZE-feature extraction, tree and random-projection indexing-based CBIR technique, is introduced which incorporates indexing after feature extraction. This reduces the retrieval time by a great extent and also saves memory. Indexing divides a search space into subspaces containing similar images together, thereby decreasing the overall retrieval time.