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Yunchao Gong, Svetlana Lazebnik, Albert Gordo, & Florent Perronnin. (2012). Iterative quantization: A procrustean approach to learning binary codes for Large-Scale Image Retrieval. TPAMI - IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(12), 2916–2929.
Abstract: This paper addresses the problem of learning similarity-preserving binary codes for efficient similarity search in large-scale image collections. We formulate this problem in terms of finding a rotation of zero-centered data so as to minimize the quantization error of mapping this data to the vertices of a zero-centered binary hypercube, and propose a simple and efficient alternating minimization algorithm to accomplish this task. This algorithm, dubbed iterative quantization (ITQ), has connections to multi-class spectral clustering and to the orthogonal Procrustes problem, and it can be used both with unsupervised data embeddings such as PCA and supervised embeddings such as canonical correlation analysis (CCA). The resulting binary codes significantly outperform several other state-of-the-art methods. We also show that further performance improvements can result from transforming the data with a nonlinear kernel mapping prior to PCA or CCA. Finally, we demonstrate an application of ITQ to learning binary attributes or “classemes” on the ImageNet dataset.
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Josep Llados, Dimosthenis Karatzas, Joan Mas, & Gemma Sanchez. (2008). A Generic Architecture for the Conversion of Document Collections into Semantically Annotated Digital Archives. Journal of Universal Computer Science, 2912–2935.
Keywords: Median Graph, Graph Embedding, Graph Matching, Structural Pattern Recognition
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Ernest Valveny, & Enric Marti. (2003). A model for image generation and symbol recognition through the deformation of lineal shapes. PRL - Pattern Recognition Letters, 24(15), 2857–2867.
Abstract: We describe a general framework for the recognition of distorted images of lineal shapes, which relies on three items: a model to represent lineal shapes and their deformations, a model for the generation of distorted binary images and the combination of both models in a common probabilistic framework, where the generation of deformations is related to an internal energy, and the generation of binary images to an external energy. Then, recognition consists in the minimization of a global energy function, performed by using the EM algorithm. This general framework has been applied to the recognition of hand-drawn lineal symbols in graphic documents.
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Jon Almazan, Albert Gordo, Alicia Fornes, & Ernest Valveny. (2014). Word Spotting and Recognition with Embedded Attributes. TPAMI - IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(12), 2552–2566.
Abstract: This article addresses the problems of word spotting and word recognition on images. In word spotting, the goal is to find all instances of a query word in a dataset of images. In recognition, the goal is to recognize the content of the word image, usually aided by a dictionary or lexicon. We describe an approach in which both word images and text strings are embedded in a common vectorial subspace. This is achieved by a combination of label embedding and attributes learning, and a common subspace regression. In this subspace, images and strings that represent the same word are close together, allowing one to cast recognition and retrieval tasks as a nearest neighbor problem. Contrary to most other existing methods, our representation has a fixed length, is low dimensional, and is very fast to compute and, especially, to compare. We test our approach on four public datasets of both handwritten documents and natural images showing results comparable or better than the state-of-the-art on spotting and recognition tasks.
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Miquel Ferrer, Ernest Valveny, & F. Serratosa. (2009). Median Graphs: A Genetic Approach based on New Theoretical Properties. PR - Pattern Recognition, 42(9), 2003–2012.
Abstract: Given a set of graphs, the median graph has been theoretically presented as a useful concept to infer a representative of the set. However, the computation of the median graph is a highly complex task and its practical application has been very limited up to now. In this work we present two major contributions. On one side, and from a theoretical point of view, we show new theoretical properties of the median graph. On the other side, using these new properties, we present a new approximate algorithm based on the genetic search, that improves the computation of the median graph. Finally, we perform a set of experiments on real data, where none of the existing algorithms for the median graph computation could be applied up to now due to their computational complexity. With these results, we show how the concept of the median graph can be used in real applications and leaves the box of the only-theoretical concepts, demonstrating, from a practical point of view, that can be a useful tool to represent a set of graphs.
Keywords: Median graph; Genetic search; Maximum common subgraph; Graph matching; Structural pattern recognition
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