{'fraction_non_alphanumeric': 0.012987012987012988, 'fraction_numerical': 0.001443001443001443, 'mean_word_length': 5.191964285714286, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Automated system for annotating and retrieving images

Automated annotation of digital images remains a highly challenging task being used for understanding of large collections of image data. Indexing and retrieval are better achieved when using the histogram as an efficient technique for content-based image retrieval domain. This paper presents an automated system that can be used for three distinct tasks: image annotation, semantic based image retrieval and content based image retrieval. The system is using an efficient annotation model called Cross Media Relevance Model for the annotation process. Image's regions are described using a vocabulary of blobs generated from image features using the K-means clustering algorithm. Using SAIAPR TC-12 Dataset of annotated images it is estimated the joint probability of generating a word given the blobs in an image. Semantic based image retrieval is performed using two models provided by the annotation model. For content based image retrieval the system is using a combination of two types of histograms: a spatial color histogram and a texture histogram based on Local Binary Pattern descriptor. Both histograms are computed in the HVC color space. The computation of the binary code associated to a Local Binary Pattern descriptor is made using the NBS distance instead of using a simple difference between colors' components.