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The possibility of integrating binary features into the bag-of-features (BoFs) model is explored. The set of binary features extracted from an image are packed into a single vector form, to yield the bag-ofbinary-features (BoBFs). The efficient BoBF feature extraction and quantisation provide fast image representation. The trade-off between accuracy and efficiency in BoBF compared with BoF is investigated through image retrieval tasks. Experimental results demonstrate that BoBF is a competitive alternative to BoF when the run-time efficiency is critical.Introduction: Image representation is a fundamental issue in many computer vision tasks, such as scene classification, image categorisation and image retrieval. These tasks are challenging on account of significant object appearance variabilities caused by non-rigidity, occlusion, background cluttering and pose and lighting changes that occur in realworld images. To address these variabilities, local image appearances are modelled using local features. The local features are very robust to the variabilities by capturing the invariant aspects of local regions.Inspired by a text-retrieval approach, bag-of-features (BoFs) [1] integrate these local features into a single vector representation. In BoF-based image representation, the set of local features extracted from an image is quantised or clustered into a vocabulary of visual words. An image is then represented by a vector of visual word frequencies obtained by indexing each feature of the image to the closest visual word. By leveraging local features, BoF-based image representation has demonstrated excellent performance for various computer vision tasks.In general, BoF builds on gradient-based local features, such as the scale-invariant feature transform (SIFT) descriptor [2]. Extracting these features is a time-intensive task, which results in a computational burden on BoF. As reported in [3], in BoF-based image representation, the majority of the computational cost is from extracting local features. This makes BoF unsuitable for some real-world applications, particularly for embedded applications in which run-time efficiency is critical. Meanwhile, efficient alternative local features, called binary features, have been recently studied. Comprised of a sequence of bit patterns, binary features are efficiently extracted by simple intensity comparisons.In this Letter, we present the bag-of-binary-features (BoBFs) by integrating the binary features into BoF. The fast feature extraction and quantisation of BoBF make it much faster than BoF in image representation. We investigate the trade-off between accuracy and efficiency of BoBF compared with BoF through image retrieval tasks. Our experimental results demonstrate that BoBF is a competitive alternative to BoF for fast image representation.

The present article relates to an experimental study on fire risks due to overcharge and external heat of ESS lithium battery. According to the experimental results of overcharge, ignition occurred as combustible gas and smoke was slowly increased after occurrence of venting, and an explosive combustion form accompanied by flame eruption and sparks was displayed as charged energy is rapidly discharged in an instant. On the other hand, according to the experimental results of external heat, as a tremendous amount of combustible gas and smoke was ignited following being discharged after occurrence of vent, the charged energy itself was rapidly reduced due to the discharged energy so that a passive combustion form was observed when compared with overcharge after occurrence of flames. According the analysis results of fire damage characteristics, differences between external heat (External flame) could be found through visual and X-ray inspections. Namely, while inside electrode plate was completely destroyed and perforation of the electrode plate was observed in the case of overcharge, fire damage of the electrode plate was not severe maintaining the form in the case of external heat.

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