Constraint and Descriptor Based Image Retrieval through Sketches with Data Retrieval using Reversible Data Hiding
Keywords:Image retrieval, Descriptor, Data retrieval, Edge extraction, Sketch-based, Grayscale, Invariance, feature extraction.
An image retrieval system includes image retrieval through sketches. Sketches act as an outline for any object with few details. "Sketch-Based Image Retrieval (SBIR)" is universally recognized as an extension of image retrieval by such rough sketching that concentrates on the main features of the object. SBIR has become an effective and popular image mining search technique as the demand for multimedia technology has grown. Due to the less precise depiction in sketches, comparing such sketches to real colorful and meaningful images becomes extremely difficult. As a solution to the captioned matter, the proposed approach incorporates Histogram Line Relationship (HLR) descriptors to facilitate constraint-based image retrieval. After pre-processing, the descriptor describes the visual features of an image. Here edge length-based constraints make SBIR powerful enough to select strong shaping edges. This approach is further enhanced to include data retrieval and is referred to as "Sketch-Based Image and Data Retrieval (SBIDR)" which even makes it more functional. Throughout image processing, the data embedding and extraction procedure are carried out using the Reversible Data Hiding (RDH) technique with an invariant grayscale version. The proposed method employs a hybrid model of image retrieval and data retrieval system with the addition of constraints and grayscale invariance. This models produce efficient outcomes in terms of retrieval.
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