CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Trademark image retrieval by local features

The challenge of abstract trademark image retrieval as a test of machine vision algorithms has attracted considerable research interest in the past decade. Current operational trademark retrieval systems involve manual annotation of the images (the current ‘gold standard’). Accordingly, current systems require a substantial amount of time and labour to access, and are therefore expensive to operate. This thesis focuses on the development of algorithms that mimic aspects of human visual perception in order to retrieve similar abstract trademark images automatically. A significant category of trademark images are typically highly stylised, comprising a collection of distinctive graphical elements that often include geometric shapes. Therefore, in order to compare the similarity of such images the principal aim of this research has been to develop a method for solving the partial matching and shape perception problem. There are few useful techniques for partial shape matching in the context of trademark retrieval, because those existing techniques tend not to support multicomponent retrieval. When this work was initiated most trademark image retrieval systems represented images by means of global features, which are not suited to solving the partial matching problem. Instead, the author has investigated the use of local image features as a means to finding similarities between trademark images that only partially match in terms of their subcomponents. During the course of this work, it has been established that the Harris and Chabat detectors could potentially perform sufficiently well to serve as the basis for local feature extraction in trademark image retrieval. Early findings in this investigation indicated that the well established SIFT (Scale Invariant Feature Transform) local features, based on the Harris detector, could potentially serve as an adequate underlying local representation for matching trademark images. There are few researchers who have used mechanisms based on human perception for trademark image retrieval, implying that the shape representations utilised in the past to solve this problem do not necessarily reflect the shapes contained in these image, as characterised by human perception. In response, a ii practical approach to trademark image retrieval by perceptual grouping has been developed based on defining meta-features that are calculated from the spatial configurations of SIFT local image features. This new technique measures certain visual properties of the appearance of images containing multiple graphical elements and supports perceptual grouping by exploiting the non-accidental properties of their configuration. Our validation experiments indicated that we were indeed able to capture and quantify the differences in the global arrangement of sub-components evident when comparing stylised images in terms of their visual appearance properties. Such visual appearance properties, measured using 17 of the proposed metafeatures, include relative sub-component proximity, similarity, rotation and symmetry. Similar work on meta-features, based on the above Gestalt proximity, similarity, and simplicity groupings of local features, had not been reported in the current computer vision literature at the time of undertaking this work. We decided to adopted relevance feedback to allow the visual appearance properties of relevant and non-relevant images returned in response to a query to be determined by example. Since limited training data is available when constructing a relevance classifier by means of user supplied relevance feedback, the intrinsically non-parametric machine learning algorithm ID3 (Iterative Dichotomiser 3) was selected to construct decision trees by means of dynamic rule induction. We believe that the above approach to capturing high-level visual concepts, encoded by means of meta-features specified by example through relevance feedback and decision tree classification, to support flexible trademark image retrieval and to be wholly novel. The retrieval performance the above system was compared with two other state-of-the-art image trademark retrieval systems: Artisan developed by Eakins (Eakins et al., 1998) and a system developed by Jiang (Jiang et al., 2006). Using relevance feedback, our system achieves higher average normalised precision than either of the systems developed by Eakins’ or Jiang. However, while our trademark image query and database set is based on an image dataset used by Eakins, we employed different numbers of images. It was not possible to access to the same query set and image database used in the evaluation of Jiang’s trademark iii image retrieval system evaluation. Despite these differences in evaluation methodology, our approach would appear to have the potential to improve retrieval effectiveness

Multi-label logo recognition and retrieval based on weighted fusion of neural features

Classifying logo images is a challenging task as they contain elements such as text or shapes that can represent anything from known objects to abstract shapes. While the current state of the art for logo classification addresses the problem as a multi-class task focusing on a single characteristic, logos can have several simultaneous labels, such as different colours. This work proposes a method that allows visually similar logos to be classified and searched from a set of data according to their shape, colour, commercial sector, semantics, general characteristics, or a combination of features selected by the user. Unlike previous approaches, the proposal employs a series of multi-label deep neural networks specialized in specific attributes and combines the obtained features to perform the similarity search. To delve into the classification system, different existing logo topologies are compared and some of their problems are analysed, such as the incomplete labelling that trademark registration databases usually contain. The proposal is evaluated considering 76,000 logos (seven times more than previous approaches) from the European Union Trademarks dataset, which is organized hierarchically using the Vienna ontology. Overall, experimentation attains reliable quantitative and qualitative results, reducing the normalized average rank error of the state-of-the-art from 0.040 to 0.018 for the Trademark Image Retrieval task. Finally, given that the semantics of logos can often be subjective, graphic design students and professionals were surveyed. Results show that the proposed methodology provides better labelling than a human expert operator, improving the label ranking average precision from 0.53 to 0.68.This work was supported by the Pattern Recognition and Artificial Intelligence Group (PRAIG) from the University of Alicante and the University Institute for Computing Research (IUII). The Conselleria d'Innovació, Universitats, Ciència I Societat Digital from Generalitat Valenciana and FEDER provided some of the computing resources used in this project through IDIFEDER/2020/003. This research was partially supported by the Conselleria de Educación, Universidades y Empleo, for the project "clasifIA" of the Escola Superior d'Art i Disseny d'Alacant

Multi-Label Logo Classification using Convolutional Neural Networks

The classification of logos is a particular case within computer vision since they have their own characteristics. Logos can contain only text, iconic images or a combination of both, and they usually include figurative symbols designed by experts that vary substantially besides they may share the same semantics. This work presents a method for multi-label classification and retrieval of logo images. For this, Convolutional Neural Networks (CNN) are trained to classify logos from the European Union TradeMark (EUTM) dataset according to their colors, shapes, sectors and figurative designs. An auto-encoder is also trained to learn representations of the input images. Once trained, the neural codes from the last convolutional layers in the CNN and the central layer of the auto-encoder can be used to perform similarity search through kNN, allowing us to obtain the most similar logos based on their color, shape, sector, figurative elements, overall features, or a weighted combination of them provided by the user. To the best of our knowledge, this is the first multi-label classification method for logos, and the only one that allows retrieving a ranking of images with these criteria provided by the user.This work is supported by the Spanish Ministry HISPAMUS project with code TIN2017-86576-R, partially funded by the EU

Multi-Object Shape Retrieval Using Curvature Trees

This work presents a geometry-based image retrieval approach for multi-object images. We commence with developing an effective shape matching method for closed boundaries. Then, a structured representation, called curvature tree (CT), is introduced to extend the shape matching approach to handle images containing multiple objects with possible holes. We also propose an algorithm, based on Gestalt principles, to detect and extract high-level boundaries (or envelopes), which may evolve as a result of the spatial arrangement of a group of image objects. At first, a shape retrieval method using triangle-area representation (TAR) is presented for non-rigid shapes with closed boundaries. This representation is effective in capturing both local and global characteristics of a shape, invariant to translation, rotation, scaling and shear, and robust against noise and moderate amounts of occlusion. For matching, two algorithms are introduced. The first algorithm matches concavity maxima points extracted from TAR image obtained by thresholding the TAR. In the second matching algorithm, dynamic space warping (DSW) is employed to search efficiently for the optimal (least cost) correspondence between the points of two shapes. Experimental results using the MPEG-7 CE-1 database of 1400 shapes show the superiority of our method over other recent methods. Then, a geometry-based image retrieval system is developed for multi-object images. We model both shape and topology of image objects including holes using a structured representation called curvature tree (CT). To facilitate shape-based matching, the TAR of each object and hole is stored at the corresponding node in the CT. The similarity between two CTs is measured based on the maximum similarity subtree isomorphism (MSSI) where a one-to-one correspondence is established between the nodes of the two trees. Our matching scheme agrees with many recent findings in psychology about the human perception of multi-object images. Two algorithms are introduced to solve the MSSI problem: an approximate and an exact. Both algorithms have polynomial-time computational complexity and use the DSW as the similarity measure between the attributed nodes. Experiments on a database of 13500 medical images and a database of 1580 logo images have shown the effectiveness of the proposed method. The purpose of the last part is to allow for high-level shape retrieval in multi-object images by detecting and extracting the envelope of high-level object groupings in the image. Motivated by studies in Gestalt theory, a new algorithm for the envelope extraction is proposed that works in two stages. The first stage detects the envelope (if exists) and groups its objects using hierarchical clustering. In the second stage, each grouping is merged using morphological operations and then further refined using concavity tree reconstruction to eliminate odd concavities in the extracted envelope. Experiment on a set of 110 logo images demonstrates the feasibility of our approach