31,108 research outputs found
Information-Theoretic Active Learning for Content-Based Image Retrieval
We propose Information-Theoretic Active Learning (ITAL), a novel batch-mode
active learning method for binary classification, and apply it for acquiring
meaningful user feedback in the context of content-based image retrieval.
Instead of combining different heuristics such as uncertainty, diversity, or
density, our method is based on maximizing the mutual information between the
predicted relevance of the images and the expected user feedback regarding the
selected batch. We propose suitable approximations to this computationally
demanding problem and also integrate an explicit model of user behavior that
accounts for possible incorrect labels and unnameable instances. Furthermore,
our approach does not only take the structure of the data but also the expected
model output change caused by the user feedback into account. In contrast to
other methods, ITAL turns out to be highly flexible and provides
state-of-the-art performance across various datasets, such as MIRFLICKR and
ImageNet.Comment: GCPR 2018 paper (14 pages text + 2 pages references + 6 pages
appendix
Adaptive image retrieval using a graph model for semantic feature integration
The variety of features available to represent multimedia data constitutes a rich pool of information. However, the plethora of data poses a challenge in terms of feature selection and integration for effective retrieval. Moreover, to further improve effectiveness, the
retrieval model should ideally incorporate context-dependent feature representations to allow for retrieval on a higher semantic level. In this paper we present a retrieval model and learning framework for the purpose of interactive information retrieval. We describe
how semantic relations between multimedia objects based on user interaction can be learnt and then integrated with visual and textual features into a unified framework. The framework models both feature similarities and semantic relations in a single graph. Querying in this model is implemented using the theory of random walks. In addition, we present ideas to implement short-term learning from relevance feedback. Systematic experimental results validate the effectiveness of the proposed approach for image retrieval. However, the model is not restricted to the image domain and could easily be employed for retrieving multimedia data (and even a combination of different domains, eg images, audio and text documents)
Revisiting Kernelized Locality-Sensitive Hashing for Improved Large-Scale Image Retrieval
We present a simple but powerful reinterpretation of kernelized
locality-sensitive hashing (KLSH), a general and popular method developed in
the vision community for performing approximate nearest-neighbor searches in an
arbitrary reproducing kernel Hilbert space (RKHS). Our new perspective is based
on viewing the steps of the KLSH algorithm in an appropriately projected space,
and has several key theoretical and practical benefits. First, it eliminates
the problematic conceptual difficulties that are present in the existing
motivation of KLSH. Second, it yields the first formal retrieval performance
bounds for KLSH. Third, our analysis reveals two techniques for boosting the
empirical performance of KLSH. We evaluate these extensions on several
large-scale benchmark image retrieval data sets, and show that our analysis
leads to improved recall performance of at least 12%, and sometimes much
higher, over the standard KLSH method.Comment: 15 page
A Compact Representation of Histopathology Images using Digital Stain Separation & Frequency-Based Encoded Local Projections
In recent years, histopathology images have been increasingly used as a
diagnostic tool in the medical field. The process of accurately diagnosing a
biopsy sample requires significant expertise in the field, and as such can be
time-consuming and is prone to uncertainty and error. With the advent of
digital pathology, using image recognition systems to highlight problem areas
or locate similar images can aid pathologists in making quick and accurate
diagnoses. In this paper, we specifically consider the encoded local
projections (ELP) algorithm, which has previously shown some success as a tool
for classification and recognition of histopathology images. We build on the
success of the ELP algorithm as a means for image classification and
recognition by proposing a modified algorithm which captures the local
frequency information of the image. The proposed algorithm estimates local
frequencies by quantifying the changes in multiple projections in local windows
of greyscale images. By doing so we remove the need to store the full
projections, thus significantly reducing the histogram size, and decreasing
computation time for image retrieval and classification tasks. Furthermore, we
investigate the effectiveness of applying our method to histopathology images
which have been digitally separated into their hematoxylin and eosin stain
components. The proposed algorithm is tested on the publicly available invasive
ductal carcinoma (IDC) data set. The histograms are used to train an SVM to
classify the data. The experiments showed that the proposed method outperforms
the original ELP algorithm in image retrieval tasks. On classification tasks,
the results are found to be comparable to state-of-the-art deep learning
methods and better than many handcrafted features from the literature.Comment: Accepted for publication in the International Conference on Image
Analysis and Recognition (ICIAR 2019
The supervised IBP: neighbourhood preserving infinite latent feature models
We propose a probabilistic model to infer supervised latent variables in the Hamming space from observed data. Our model allows simultaneous inference of the number of binary latent variables, and their values. The latent variables preserve neighbourhood structure of the data in a sense that objects in the same semantic concept have similar latent values, and objects in different concepts have dissimilar latent values. We formulate the supervised infinite latent variable problem based on an intuitive principle of pulling objects together if they are of the same type, and pushing them apart if they are not. We then combine this principle with a flexible Indian Buffet Process prior on the latent variables. We show that the inferred supervised latent variables can be directly used to perform a nearest neighbour search for the purpose of retrieval. We introduce a new application of dynamically extending hash codes, and show how to effectively couple the structure of the hash codes with continuously growing structure of the neighbourhood preserving infinite latent feature space
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