Intuitivno pretraživanje baze slike kao potpora označavanju slika

Image annotation is typically performed manually since automatic image annotation approaches have not matured yet to be used in practice. Consequently, image annotation is a labour intensive and time consuming task. In this paper, we show how an image browsing system can be employed to support efficient and effective (manual) annotation of image databases. In contrast to other approaches, which typically present images in a linear fashion, we employ a visualisation where images are arranged by mutual visual similarity. Since in this arrangement similar images are close to each other, they can easily be selected and annotated together. Organisation on a grid layout prevents image overlap and thus contributes to a clear presentation. Large image databases are handled through a hierarchical data structure where each image in the visualisation can correspond to a cluster of images that can be expanded by the user. Experimental results indicate that annotation can be performed faster on our proposed system.Označavanje slika obično se obavlja ručno jer automatski pristupi još nisu dovoljno kvalitetni kako bi se koristili u praksi. Zbog toga je označavanje slika u bazi vremenski zahtjevno. U ovom radu pokazat ćemo kako se sustav za pregled slika u bazi može koristiti kao učinkovita potpora ručnom označavanju slika. Za razliku od drugih pristupa, koji prikazuju slike u linearnom poretku, korištena je vizualizacija u kojoj su slike složene po međusobnoj sličnosti. Budući da su na taj način slične slike međusobno blizu jedna drugoj, lako ih je selektirati i zajednički označiti. Slike su organizirane u mrežni prikaz radi sprječavanja preklapanja i jasnije prezentacije. Velike baze podataka organizirane su u hijerarhijsku strukturu gdje svaka slika u pojedinoj vizualizaciji može pripadati skupu slika čiji prikaz korisnik po želji može proširivati. Rezultati provedenih eksperimenata pokazuju da se označavanje slika pomoću predloženog sustava može obavljati brže nego na uobičajeni način

Novel CBIR System Based on Ripplet Transform Using Interactive Neuro-Fuzzy Technique

Content Based Image Retrieval (CBIR) system is an emerging research area in effective digital data management and retrieval paradigm. In this article, a novel CBIR system based on a new Multiscale Geometric Analysis (MGA)-tool, called Ripplet Transform Type-I (RT) is presented. To improve the retrieval result and to reduce the computational complexity, the proposed scheme utilizes a Neural Network (NN) based classifier for image pre-classification, similarity matching using Manhattan distance measure and relevance feedback mechanism (RFM) using fuzzy entropy based feature evaluation technique. Extensive experiments were carried out to evaluate the effectiveness of the proposed technique. The performance of the proposed CBIR system is evaluated using a 2 £ 5-fold cross validation followed by a statistical analysis. The experimental results suggest that the proposed system based on RT, performs better than many existing CBIR schemes based on other transforms, and the difference is statistically significant

2D Images Map Warping for Improved User Interaction

Abstract In this paper, we suggest an interaction model designed to fit users' expectations in front of an image retrieval system. A lightweight relevance feedback strategy, working directly on the 2D projection of image features, allows the user to spatially navigate the media collection maintaining the real-time constraint. A preliminary evaluation of this relevance feedback strategy shows good performance compared with other known approaches

Content-Based Image Retrieval Based on Electromagnetism-Like Mechanism

Recently, many researchers in the field of automatic content-based image retrieval have devoted a remarkable amount of research looking for methods to retrieve the best relevant images to the query image. This paper presents a novel algorithm for increasing the precision in content-based image retrieval based on electromagnetism optimization technique. The electromagnetism optimization is a nature-inspired technique that follows the collective attraction-repulsion mechanism by considering each image as an electrical charge. The algorithm is composed of two phases: fitness function measurement and electromagnetism optimization technique. It is implemented on a database with 8,000 images spread across 80 classes with 100 images in each class. Eight thousand queries are fired on the database, and the overall average precision is computed. Experimental results of the proposed approach have shown significant improvement in the retrieval performance in regard to precision

Serendipitous Exploration of Large-scale Product Catalogs

Abstract-Online shopping has developed to a stage where catalogs have become very large and diverse. Thus, it is a challenge to present relevant items to potential customers within a very few interactions. This is even more so when users have no defined shopping objectives but operate in an opportunistic mindset. This problem is often tackled by recommender systems. However, these systems rely on consistent user interaction patterns to predict items of interest. In contrast, we propose to adapt the classical information retrieval (IR) paradigm for the purpose of accessing catalog items in a context of un-predictable user interaction. Accordingly, we present a novel information access strategy based on the notion of interest rather than relevance. We detail the design of a scalable browsing system including learning capabilities joint with a limited-memory model. Our approach enables locating interesting items within a few steps while not requiring good quality descriptions. Our system allows customer to seamlessly change browsing objectives without having to start explicitly a new session. An evaluation of our approach based on both artificial and real-life datasets demonstrates its efficiency in learning and adaptation. I. MOTIVATION The emergence of online shopping has offered new opportunities to propose services and products to customers. Currently, many online shops are not anymore restricted to a certain category of products. For example Amazon, initially focused on cultural and entertainment media (books, music, and video), is now offering products as diverse as home appliances or jewelry. Even more crucial, we usually find thousands of items within a product category, e.g. 38 million books and 3,5 million jewelry items on Amazon. Both the breadth of product lines and the depth within a product line not only boost the volume of the catalogs but also make it difficult for the customer to find products of interest without an accurate search protocol. Presenting relevant products to potential customers is the goal of recommender systems. Independent of their type (collaborative filtering systems, content-based recommender, etc), recommender systems usually operate on a user profile gained from previous shopping sessions. For this reason, recommender systems suffer from the cold-start problem, when new users and/or new products appear In contrast to the above, our approach does not require the definition of a user profile nor it imposes specific search sessions with pre-defined objectives. In other words, we present an efficient product access strategy enabling intuitive browsing by estimating the user's intention from his/her input to the system and displaying items that are considered as most interesting to him/her (and thus likely to be purchased). Our new information access strategy is based on the notion of current interest rather than on the notion of relevance classically used in Information Retrieval (O1) We accommodate serendipity. We assume no pre-defined (fixed) objective of the user's chain of actions; (O2) The system matches classic (simple) interaction models; (O3) The system is scalable in terms of the volume of the product catalog. Our approach results in an interactive navigation system, which let the user operate naturally over the product catalog while swiftly reacting to changes in the browsing objectives. The major difference with earlier approaches is a rapidly adapting system, that copes with radical changes, and is scalable to operate over realistic-scale product catalogs. The remainder of the paper is structured as follows: in section II, we discuss relevant approaches for information characterisation and content access strategies in large repositories. In section III, we present our interaction model, which describes the type of interaction that is expected from the user and what information is carried over with this interaction. We formalise our navigation model, anticipating functional issues in section IV. In particular, we review its properties ensuring scalability and compatibility with other models. In section V, we propose a comprehensive assessment of the performance of our model in an adaptive browsing scenario. At every browsing step, the system aims at displaying the most useful items to the user with respect to past interaction. Although our study includes an inherent temporal dimension, which makes the evaluation context different from that of classical searc

Visualização de Informação aplicada à compreensão de resultados de Recuperação de Imagens Baseada em Conteúdo

A Visualização de Informação se propõe a representar dados abstratos graficamente, de modo a melhorar a compreensão dos mesmos. Sistemas de recuperação de imagens baseada em conteúdo (Content-Based Image Retrieval - CBIR) produzem grandes volumes de dados, que, muitas vezes, são exibidos de modo pouco compreensível. Tendo em vista este cenário, este artigo tem como objetivo propor e avaliar técnicas de visualização de informação que otimizem a exibição de resultados de sistemas de CBIR. Foram desenvolvidas duas técnicas bidimensionais e duas tridimensionais. Por meio de avaliação com usuários, constatou-se que as técnicas bidimensionais propostas foram as mais eficientes em melhorar a compreensão dos resultados no contexto analisado

Efficient Image Tagging

Tato práce se zabývá efektivním tagováním fotografií. Konkrétně se zaměřuje na uspořádání jednotlivých fotografií tak, aby tvořily shluky podle svých vlastností a usnadnily tak výběr podobných fotografií, kterým uživatel může efektivně přiřazovat společné tagy zároveň. K tomuto účelu jsou v práci zkoumány známé techniky zobrazování kolekcí fotografií podle jejich vlastností a s tím související metody redukce dimenzionality. Ze zmiňovaných jsou vybrány a otestovány nejvhodnější možnosti. Tato práce navrhuje nový způsob zobrazování kolekcí fortografií na 2D obrazovce, která kombinuje použití časové osy a seskupování podle podobnosti (Timeline projekce). Pro optimální projekci uskupení v mnohorozměrném prostoru příznakových vektorů na 2-rozměrnou obrazovku je v této práci použita metoda redukce dimenzionality nazvaná t-Distributed Stochastic Neighbour Embedding (t-SNE). Jsou popsány různé modifikace t-SNE a způsoby, jak ji kombinovat s časovou osou, a zvolená modifikace je implementována formou webového rozhraní a kvalitativně vyhodnocena experimentem. Na závěr jsou navrženy možnosti pokračování výzkumu.This thesis investigates efficient manual image tagging approaches. It specifically focuses on organising images into clusters depending on their content, and thus on simplifying the selection of similar photos. Such selections may be efficiently tagged with common tags. The thesis investigates known techniques for visualisation of image collections according to the image content, together with dimensionality reduction methods. The most suitable methods are considered and evaluated. The thesis proposes a novel method for presenting image collections on 2D displays which combines a timeline with similarity grouping (Timeline projection). This method utilizes t-Distributed Stochastic Neighbour Embedding (t-SNE) for otpimally projecting groupings in high dimensional feature spaces onto the low-dimensional screen. Various modifications of t-SNE and ways to combine it with the timeline are discussed and chosen combination is implemented as a web interface and is qualitatively evaluated in a user study. Possible directions of further research on the subject are suggested.