Sketch-based 3D shape retrieval has become an important research topic in content-based 3D object retrieval. To foster this research area, two Shape Retrieval Contest (SHREC) tracks on this topic have been organized by us in 2012 and 2013 based on a small-scale and large-scale benchmarks, respectively. Six and five (nine in total) distinct sketch-based 3D shape retrieval method have competed each other in these two contests, respectively. To measure and compare the performance of the top participating and other existing promising sketch-based 3D shape retrieval methods and solicit the state-of-the-art approaches, we perform a more comprehensive comparison of fifteen best (four top participating algorithms and eleven additional state-of-the-art methods) retrieval methods by completing the evaluation of each method on both benchmarks. The benchmarks, results, and evaluation tools for the two tracks are publicly available on our websites [1,2]
A natural alternative for querying in an image retrieval system is by simply drawing what one has in mind. Indeed, drawing was the primitive means of communication between humans. One of the goals of an image retrieval scenario is to provide users a simple modality for querying. Thereby, a drawing means a simple hand-drawn sketch composed only of strokes that users can do easily, lacking color or texture. Examples of hand-drawn sketches are shown in Figure 1. This querying modality leads to the sketch based image retrieval problem (SBIR) which is a challenging problem because of two main reasons: (i) images that we want to retrieve are not sketches, (ii) query sketches show certain level of ambiguity by nature that may make a method get confused easily. Consequently, state-of-the-arts SBIR approaches [2, 4] still show low performance.Therefore, taking some ideas of the human visual perception, we present a novel method for sketch based image retrieval. Our method, is based on detecting the occurrences of mid-level patterns on a sketch. To this end, we figure out a set of patterns (learned keyshapes) by means of an unsupervised learning process. We then build a histogram that counts the occurrences of the patterns in the underlying sketch. The histogram is built using soft-voting, spatial division and squared root normalization. We show new state-of-the-art results in two available datasets doubling the precision achieved by current methods.Our proposal consists of two stages ( Figure 2): (1) figure out a set of keyshapes, (2) generate the LKS descriptors based on the detected set of keyshapes, that will be used later for similarity search.
This paper introduces S-HELO (Soft-Histogram of Edge Local Orientations), an outperforming method for describing images in the context of sketch based image retrieval (SBIR). This proposal exploits the advantages provided by the HELO descriptor for describing sketches, and improves significantly its performance by using a soft computation of local orientations and taking into account spatial information. We experimentally demonstrate that a soft computation process together with a local estimation of orientations are very suitable for describing sketches in the context of image retrieval. Indeed, our results show that S-HELO significantly outperforms not only HELO but also classical orientation-based descriptors as HOG. We also show that S-HELO performs very close to the optimal when what we want to retrieve are target images. Moreover, our proposal also shows an outstanding performance for similarity search, i.e., retrieving images that belong to the same category of the query sketch.Index Terms-Sketch based image retrieval, sketch descriptors, orientation histograms.
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