Topic
Probabilistic latent semantic analysis
About: Probabilistic latent semantic analysis is a research topic. Over the lifetime, 2884 publications have been published within this topic receiving 198341 citations. The topic is also known as: PLSA.
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07 Nov 2005
TL;DR: A novel approach for describing image color semantic including regional and global semantic description is developed and presented, which allows the users to query images with emotional semantic words.
Abstract: Describing images in semantic terms is an important and challenging problem in content-based image retrieval. According to the strong relationship between colors and human emotions, an emotional semantic query model based on image color semantic description is proposed in this study. First, images are segmented into regions through a new color image segmentation algorithm. Then, term sets are generated through a fuzzy clustering algorithm so that colors can be interpreted in semantic terms. We extend the method to extract the color semantic of image regions, and develop a novel approach for describing image color semantic including regional and global semantic description. Finally, we present an image query scheme through image color semantic description, which allows the users to query images with emotional semantic words. Experimental results demonstrate the effectiveness of our approach.
57 citations
Proceedings Article•
26 Jun 2012TL;DR: In this article, a unified framework for structured prediction with latent variables is proposed, which includes hidden conditional random fields and latent structured support vector machines as special cases, and a local entropy approximation for this general formulation using duality is derived.
Abstract: In this paper we propose a unified framework for structured prediction with latent variables which includes hidden conditional random fields and latent structured support vector machines as special cases. We describe a local entropy approximation for this general formulation using duality, and derive an efficient message passing algorithm that is guaranteed to converge. We demonstrate its effectiveness in the tasks of image segmentation as well as 3D indoor scene understanding from single images, showing that our approach is superior to latent structured support vector machines and hidden conditional random fields.
57 citations
TL;DR: This paper presents a principled approach to learning a semantic vocabulary from a large amount of video words using Diffusion Maps embedding, and conjecture that the mid-level features produced by similar video sources must lie on a certain manifold.
57 citations
TL;DR: Evidence is provided for qualifying LSA cosine similarities not only as a linguistic measure, but also as a cognitive similarity measure, as it is also shown that other DSMs can outperform LSA as a predictor of priming effects.
Abstract: In distributional semantics models (DSMs) such as latent semantic analysis (LSA), words are represented as vectors in a high-dimensional vector space. This allows for computing word similarities as the cosine of the angle between two such vectors. In two experiments, we investigated whether LSA cosine similarities predict priming effects, in that higher cosine similarities are associated with shorter reaction times (RTs). Critically, we applied a pseudo-random procedure in generating the item material to ensure that we directly manipulated LSA cosines as an independent variable. We employed two lexical priming experiments with lexical decision tasks (LDTs). In Experiment 1 we presented participants with 200 different prime words, each paired with one unique target. We found a significant effect of cosine similarities on RTs. The same was true for Experiment 2, where we reversed the prime-target order (primes of Experiment 1 were targets in Experiment 2, and vice versa). The results of these experiments confirm that LSA cosine similarities can predict priming effects, supporting the view that they are psychologically relevant. The present study thereby provides evidence for qualifying LSA cosine similarities not only as a linguistic measure, but also as a cognitive similarity measure. However, it is also shown that other DSMs can outperform LSA as a predictor of priming effects.
57 citations
01 Jan 2003
TL;DR: Initial results on live collections suggest that this graph-based algorithm may offer performance comparable to latent semantic indexing (LSI), while avoiding some of that technique’s computational pitfalls.
Abstract: The authors present a graph-based algorithm for searching potentially large collections of unstructured data, and discuss its implementation as a search engine designed to offer advanced relevance feedback features to users who may have limited familiarity with search tools. The technique, which closely resembles the spreading activation network model described by Scott Preece, uses a term-document matrix to generate a bipartite graph of term and document nodes representing the document collection. This graph can be searched by a simple recursive procedure that distributes energy from an initial query node. Nodes that acquire energy above a specified threshold comprise the result set. Initial results on live collections suggest that this technique may offer performance comparable to latent semantic indexing (LSI), while avoiding some of that technique’s computational pitfalls. Both the algorithm and its implementation in a production Web environment are discussed.
57 citations