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A Training-Free Approach for Generic Object Detection

B. Baheti,S. Talbar,S. Gajre

Published 2019 in Journal of the Institution of Electronics and Telecommunication Engineers

ABSTRACT

We present an approach for generic object detection using single query image for finding and locating visually similar objects from target images. The key challenge here is describing an object class using only one query without any training. Our approach is based on computation of Local Self-Similarity descriptors which captures local internal geometric layout within an image and is good representative of object class. We propose to use only predefined landmark points from query image which significantly improves performance of detection. We also present few novel ideas for selection of informative descriptors from the set of all descriptors of the test image to reduce computational expense in feature matching. The algorithm yields Hough-style similarity surface indicating likelihood of presence of the query object at every location. Presence and location of objects are finalized by employing two significance tests followed by non-maxima suppression. We evaluate results of the proposed approach on UIUC car dataset and achieve higher accuracy in comparison with earlier training-free approaches. Results are also analysed on ETHZ shape classes dataset which accounts for large intra-class variations, scale variations, and clutter. Performance of detection on this challenging dataset having diverse contexts demonstrates robustness and efficacy of the algorithm.

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