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  • 1
    Language: English
    In: Image and Vision Computing, 2010, Vol.28(8), pp.1309-1326
    Description: Color features are the key-elements widely used in content-analysis and retrieval. However, most of them show severe limitations and drawbacks due to their inefficiency of modeling the human visual system with respect to color perception. Moreover, they cannot characterize all the properties of the color composition in a visual scenery. In this paper we present a perceptual color feature, which describes all major properties of prominent colors both in spatial and color domains. In accordance with the well-known law, we adopt a global, top-down approach in order to model (see) the whole color composition before its parts and in this way we can avoid the problems of pixel-based approaches. In color domain the dominant colors are extracted along with their global properties and quad-tree decomposition partitions the image so as to characterize the spatial color distribution (SCD). We propose two efficient SCD descriptors; the proximity histograms, which distill the histogram of inter-color distances and the proximity grids, which cumulate the spatial co-occurrence of colors in a 2D grid. Both approaches are configurable and provide means of modeling SCD in a scalar and directional way. Combination of the extracted global and spatial properties forms the final descriptor, which is unbiased and robust to non-perceivable color elements in both spatial and color domains. Finally a penalty-trio model fuses all color properties in a similarity distance computation during retrieval. Experimental results approve the superiority of the proposed technique against powerful global and spatial color descriptors.
    Keywords: Perceptual Color Descriptor ; Human Visual System ; Content-Based Image Indexing and Retrieval ; Spatial Color Distribution ; Engineering ; Applied Sciences
    ISSN: 0262-8856
    E-ISSN: 1872-8138
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  • 2
    Language: English
    In: Image and Vision Computing, October 2018, Vol.78, pp.73-83
    Description: Managing the water quality of freshwaters is a crucial task worldwide. One of the most used methods to biomonitor water quality is to sample benthic macroinvertebrate communities, in particular to examine the presence and proportion of certain species. This paper presents a benchmark database for automatic visual classification methods to evaluate their ability for distinguishing visually similar categories of aquatic macroinvertebrate taxa. We make publicly available a new database, containing 64 types of freshwater macroinvertebrates, ranging in number of images per category from 7 to 577. The database is divided into three datasets, varying in number of categories (64, 29, and 9 categories). Furthermore, in order to accomplish a baseline evaluation performance, we present the classification results of Convolutional Neural Networks (CNNs) that are widely used for deep learning tasks in large databases. Besides CNNs, we experimented with several other well-known classification methods using deep features extracted from the data.
    Keywords: Biomonitoring ; Fine-Grained Classification ; Benthic Macroinvertebrates ; Deep Learning ; Convolutional Neural Networks ; Engineering ; Applied Sciences
    ISSN: 0262-8856
    E-ISSN: 1872-8138
    Source: ScienceDirect Journals (Elsevier)
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