Enhancing Sketch-Based Image Retrieval by Re-Ranking and Relevance Feedback
Abstract of Sketch-Based Image Retrieval by Re-Ranking and Feedback
Sketch-Based Image Retrieval by Re-Ranking and Relevance Feedback. A sketch-based image retrieval often needs to optimize the tradeoff between efficiency and precision.
However, the performance can be affected by quantization errors.Moreover, the ambiguousness of user-provided examples may also degrade the performance, when compared with traditional image retrieval methods. image retrieval systems that preserve the index structure are challenging.
In this paper, we propose an effective image retrieval approach with re-ranking and relevance feedback schemes.
Our approach makes full use of the semantics in query sketches and the top ranked images of the initial results.
We also apply relevance feedback to find more relevant images for the input query sketch.
The integration of the two schemes results in mutual benefits and improves the performance of the sketch-based image retrieval. Content-based image retrieval (CBIR) mainly uses text and images for queries. However, it is often not possible to precisely describe the content of the desired images using plain text. Additionally, obtaining image examples that exactly match a user’s search intentions is not a trivial task. Query sketches drawn by users can effectively describe the aim of a search. Therefore, query-by-sketch is an effective method when text description or query examples are unavailable.
Sketch-based image retrieval (SBIR) methods use a hand-drawn sketch composed of simple strokes or lines to fulfill the image retrieval task. In a user’s visual perception, the most informative lines in an image are the contours. A sketch is generally a rough description of an object’s shape and contours. The sketch does not need to be artistic, and is simply the user’s rough impression of the intended object.
