In this paper, we present a system for sketch classification and similarity search. We used deep convolution neural networks (ConvNets), state of the art in the field of image recognition. They enable both classification and medium/highlevel features extraction. We make use of ConvNets features as a basis for similarity search using k-Nearest Neighbors (kNN). Evaluation are performed on the TU-Berlin benchmark. Our main contributions are threefold: first, we use ConvNets in contrast to most previous approaches based essentially on hand crafted features. Secondly, we propose a ConvNet that is both more accurate and lighter/faster than the two only previous attempts at making use of ConvNets for handsketch recognition. We reached an accuracy of 75.42%. Third, we shown that similarly to their application on natural images, ConvNets allow the extraction of medium-level and high-level features (depending on the depth) which can be used for similarity search. 1

No abstract

No abstract

This paper introduces the IMOTION system, a contentbased video retrieval engine, which is our proposed contribution for the 2015 Video Search Showcase. IMOTION is based on the Cineast system and was originally designed as a sketch-based video retrieval engine supporting multiple query paradigms. For the Video Search Showcase, it has been extended by dedicated motion features to allow for the specification of motion within a video sequence. For vector space retrieval, the IMOTION system exploits a large variety of low-level image and video features, as well as high-level spatial and temporal features that can all be jointly used in any combination. Thus, the IMOTION system supports query-by-sketch interactions (users provide sketches of video frames), motion queries (users specify motion across frames via partial flow fields), query-by-example (based on images) and any combination of these, and provides support for relevance feedback.

No abstract