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The computation of good image descriptors is key to the instance retrieval problem and has been the object of much recent interest from the multimedia research community. With deep learning becoming the dominant approach in computer vision, the use of representations extracted from Convolutional Neural Nets (CNNs) is quickly gaining ground on Fisher Vectors (FVs) as favoured state-of-the-art global image descriptors for image instance retrieval. While the good performance of CNNs for image classification are unambiguously recognised, which of the two has the upper hand in the image retrieval context is not entirely clear yet.In this work, we propose a comprehensive study that systematically evaluates FVs and CNNs for image retrieval. The first part compares the performances of FVs and CNNs on multiple publicly available data sets. We investigate a number of details specific to each method. For FVs, we compare sparse descriptors based on interest point detectors with dense single-scale and multi-scale variants. For CNNs, we focus on understanding the impact of depth, architecture and training data on retrieval results. Our study shows that no descriptor is systematically better than the other and that performance gains can usually be obtained by using both types together. The second part of the study focuses on the impact of geometrical transformations such as rotations and scale changes. FVs based on interest point detectors are intrinsically resilient to such transformations while CNNs do not have a built-in mechanism to ensure such invariance. We show that performance of CNNs can quickly degrade in presence of rotations while they are far less affected by changes in scale. We then propose a number of ways to incorporate the required invariances in the CNN pipeline.Overall, our work is intended as a reference guide offering practically useful and simply implementable guidelines to anyone looking for state-of-the-art global descriptors best suited to their specific image instance retrieval problem. * V. Chandrasekhar, J. Lin and O. Morère contributed equally to this work.

Thiamphenicol and florfenicol are two phenicol antibiotics commonly used in aquaculture. Photodegradation experiments on these phenicols were performed in aqueous solutions under irradiation of different light sources. We found under UV-vis irradiation (lamda >200 nm) they photodegraded the fastest in seawater, followed by pure water and freshwater, whereas under solar or simulated sunlight (lamda >290 nm), they photodegraded in freshwater only. The effects of Cl- (the dominant seawater constituent), humic acids (HA, main constituents in freshwater) and other water constituents on the photodegradation of the antibiotics as a function of different light sources were studied so as to interpret the light-source-dependent effects of different waters. Under UV-vis irradiation, Cl- was found to promote singlet oxygen ((1)O2) formation and accelerated the photodegradation of phenicols, whereas the phenicols did not photolyze under simulated solar irradiation, irrespective of Cl-. In contrast, the presence of HA inhibited phenicol photolysis under UV-vis irradiation through competitive photoabsorption, but HA photosensitized degradation under simulated solar irradiation. Under UV-vis irradiation, the wavelength-averaged (200-290 nm) quantum yields for thiamphenicol and florfenicol in pure water were 0.022 +/- 0.001 and 0.029 +/- 0.001, respectively. Their solar photolytic half-lives in freshwater were 186 +/- 17 h and 99 +/- 16 h, respectively. UV-vis photodegradation intermediates were identified by HPLC-MS/MS, and degradation pathways were proposed. These involve photoinduced hydrolysis, dechlorination, self-sensitized photo-oxidation processvia (1)O2, and chlorination. These results are of importance toward the goal of assessing the persistence of phenicols in wastewater treatment and the environment.

Compact descriptors for visual search (CDVS) is a recently completed standard from the ISO/IEC moving pictures experts group (MPEG). The primary goal of this standard is to provide a standardized bitstream syntax to enable interoperability in the context of image retrieval applications. Over the course of the standardization process, remarkable improvements were achieved in reducing the size of image feature data and in reducing the computation and memory footprint in the feature extraction process. This paper provides an overview of the technical features of the MPEG-CDVS standard and summarizes its evolution.

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This paper provides an overview of the on-going compact descriptors for video analysis standard (CDVA) from the ISO/IEC moving pictures experts group (MPEG). MPEG-CDVA targets at defining a standardized bitstream syntax to enable interoperability in the context of video analysis applications. During the developments of MPEG-CDVA, a series of techniques aiming to reduce the descriptor size and improve the video representation ability have been proposed. This article describes the new standard that is being developed and reports the performance of these key technical contributions.1. 2.5Mbps is the standard bitrate for 720P video with standard frame rate (30fps).