Abstract
Developing an accurate and efficient Sketch-Based Image Retrieval (SBIR) method in determining the resemblances between the user's query and image stream has been a never-ending quest in digital data communication era. The main challenge is to overcome the asymmetry between a binary sketch and a full-color image. We introduce a unique sketch board mining method to recover the online web images. This image conceptual retrieval is performed by matching the sketch query with the relevant terminology of selected images. A systematic sequence is followed, including the sketch drawing by the user in interpreting its geometrical shape of the conceptual form based on annotation metadata matching technique achieved automatically from Google engines, indexing and clustering the selected images via data mining. The sketch mining board being built in dynamic drawing state used a set of features to generalize sketch board conceptualization in semantic level. Images from the global repository are retrieved via a semantic match of the user's sketch query with them. Excellent retrieval of hand-drawn sketches is found to achieve the recall rate within 0.1 to 0.8 and a precision rate is 0.7 to 0.98. The proposed technique solved many problems that stat-of-art suffered from SBIR (e.g. scaling, transport, imperfect) sketch. Furthermore, it is demonstrated that the proposed technique allowed us to exploit high-level features to search the web effectively and may constitute a basis for efficient and precise image recovery tool.
Highlights
There has been a wide interest and progress on computer aided retrieval of media data
This evaluation shows that the performance of the keyshape mining based proposal increases the retrieval effectiveness, which satisfy correctly identified images True Positive (TP) for accuracy of retrieved images
The sketch specification is easy for transforming into the general ontological concept in a way that reduces False Negative (FN) images
Summary
There has been a wide interest and progress on computer aided retrieval of media data. Unlike other classical methods to detect shapes similar to the present sketch, the proposed technique avoids the efficiency issues regarding shape detection of low-level features and extracts them by for example, the Hough transform or canny (Dube and Zell, 2011; Abdulbaqi et al, 2014). In this communication, we report a novel method to input sketch and retrieved images based on the sketch online.
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