A words-of-interest model of sketch representation for image retrieval

In this paper we propose a method for sketch-based image retrieval. Sketch is a magical medium which is capable of conveying semantic messages for user. It’s in accordance with user’s cognitive psychology to retrieve images with sketch. In order to narrow down the semantic gap between the user and the images in database, we preprocess all the images into sketches by the coherent line drawing algorithm. During the process of sketches extraction, saliency maps are used to filter out the redundant background information, while preserve the important semantic information. We use a variant of Words-of-Interest model to retrieve relevant images for the user according to the query. Words-of-Interest (WoI) model is based on Bag-ofvisual Words (BoW) model, which has been proven successfully for information retrieval. Bag-of-Words ignores the spatial relationships among visual words, which are important for sketch representation. Our method takes advantage of the spatial information of the query to select words of interest. Experimental results demonstrate that our sketch-based retrieval method achieves a good tradeoff between retrieval accuracy and semantic representation of users’ query

Semantic Graph for Zero-Shot Learning

Zero-shot learning aims to classify visual objects without any training data via knowledge transfer between seen and unseen classes. This is typically achieved by exploring a semantic embedding space where the seen and unseen classes can be related. Previous works differ in what embedding space is used and how different classes and a test image can be related. In this paper, we utilize the annotation-free semantic word space for the former and focus on solving the latter issue of modeling relatedness. Specifically, in contrast to previous work which ignores the semantic relationships between seen classes and focus merely on those between seen and unseen classes, in this paper a novel approach based on a semantic graph is proposed to represent the relationships between all the seen and unseen class in a semantic word space. Based on this semantic graph, we design a special absorbing Markov chain process, in which each unseen class is viewed as an absorbing state. After incorporating one test image into the semantic graph, the absorbing probabilities from the test data to each unseen class can be effectively computed; and zero-shot classification can be achieved by finding the class label with the highest absorbing probability. The proposed model has a closed-form solution which is linear with respect to the number of test images. We demonstrate the effectiveness and computational efficiency of the proposed method over the state-of-the-arts on the AwA (animals with attributes) dataset.Comment: 9 pages, 5 figure

Topic Uncovering and Image Annotation via Scalable Probit Normal Correlated Topic Models

Topic uncovering of the latent topics have become an active research area for more than a decade and continuous to receive contributions from all disciplines including computer science, information science and statistics. Since the introduction of Latent Dirichlet Allocation in 2003, many intriguing extension models have been proposed. One such extension model is the logistic normal correlated topic model, which not only uncovers hidden topic of a document, but also extract a meaningful topical relationship among a large number of topics. In this model, the Logistic normal distribution was adapted via the transformation of multivariate Gaussian variables to model the topical distribution of documents in the presence of correlations among topics. In this thesis, we propose a Probit normal alternative approach to modelling correlated topical structures. Our use of the Probit model in the context of topic discovery is novel, as many authors have so far concentrated solely of the logistic model partly due to the formidable inefficiency of the multinomial Probit model even in the case of very small topical spaces. We herein circumvent the inefficiency of multinomial Probit estimation by using an adaptation of the Diagonal Orthant Multinomial Probit (DO-Probit) in the topic models context, resulting in the ability of our topic modelling scheme to handle corpuses with a large number of latent topics. In addition, we extended our model and implement it into the context of image annotation by developing an efficient Collapsed Gibbs Sampling scheme. Furthermore, we employed various high performance computing techniques such as memory-aware Map Reduce, SpareseLDA implementation, vectorization and block sampling as well as some numerical efficiency strategy to allow fast and efficient sampling of our algorithm