Guest Editorial: Learning Multimedia for Real World Applications

Multimedia data, as vivid and comprehensive, exist everywhere in our daily lives, in communication, education, manufacturing and service industries, and so on. Thus, how to improve the learning of multimedia data for real world applications has attracted widespread interests in the academy circle. This issue consists of 12 papers, which are briefly discussed as follows. Three out of these 12 papers focus on developing feature representation to improve the performance of image retrieval, near duplicate image detection and video classification respectively. Sketch-based Image Retrieval (SBIR), which uses simple edge or contour images, is one important branch of Content-based Image Retrieval. However, SBIR is more difficult than CBIR due to the lack of visual information, this makes the Bag-of-Words (BoW) or codebook in SBIR hard to construct. The paper entitled BSketch4Image: A Novel Framework for Sketch-Based Image Retrieval Based on Product Quantization with Coding Residuals^ (10.1007/s11042-015-2645-y) proposes a novel SBIR framework based on Product Quantization (PQ) with sparse coding (SC) to construct an optimized codebook. In BEfficient Multimed Tools Appl (2016) 75:2413–2417 DOI 10.1007/s11042-016-3286-5