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We present an image retrieval system driven by free-hand sketched queries depicting shape. We introduce Gradient Field HoG (GF-HOG) as a depiction invariant image descriptor, encapsulating local spatial structure in the sketch and facilitating efficient codebook based retrieval. We show improved retrieval accuracy over 3 leading descriptors (Self Similarity, SIFT, HoG) across two datasets (Flickr160,...
This paper proposes an efficient approach for object classification. This method bases on bag-of-features classification framework and extends the limits of it. It applies modified spatial PACT as local feature descriptor, which can efficiently catch image patch's characteristic. In order to address the speed bottleneck of codebook creation, extremely randomized clustering forest is used to create...
Object models based on bag-of-words representations can achieve state-of-the-art performance for image classification and object localization tasks. However, as they consider objects as loose collections of local patches they fail to accurately locate object boundaries and are not able to produce accurate object segmentation. On the other hand, Markov random field models used for image segmentation...
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