Semantic Concept Co-Occurrence Patterns for Image Annotation and Retrieval.

Describing visual image contents by semantic concepts is an effective and straightforward way to facilitate various high level applications. Inferring semantic concepts from low-level pictorial feature analysis is challenging due to the semantic gap problem, while manually labeling concepts is unwise because of a large number of images in both online and offline collections. In this paper, we present a novel approach to automatically generate intermediate image descriptors by exploiting concept co-occurrence patterns in the pre-labeled training set that renders it possible to depict complex scene images semantically. Our work is motivated by the fact that multiple concepts that frequently co-occur across images form patterns which could provide contextual cues for individual concept inference. We discover the co-occurrence patterns as hierarchical communities by graph modularity maximization in a network with nodes and edges representing concepts and co-occurrence relationships separately. A random walk process working on the inferred concept probabilities with the discovered co-occurrence patterns is applied to acquire the refined concept signature representation. Through experiments in automatic image annotation and semantic image retrieval on several challenging datasets, we demonstrate the effectiveness of the proposed concept co-occurrence patterns as well as the concept signature representation in comparison with state-of-the-art approaches

Context guided belief propagation for remote sensing image classification.

We propose a context guided belief propagation (BP) algorithm to perform high spatial resolution multispectral imagery (HSRMI) classification efficiently utilizing superpixel representation. One important characteristic of HSRMI is that different land cover objects possess a similar spectral property. This property is exploited to speed up the standard BP (SBP) in the classification process. Specifically, we leverage this property of HSRMI as context information to guide messages passing in SBP. Furthermore, the spectral and structural features extracted at the superpixel level are fed into a Markov random field framework to address the challenge of low interclass variation in HSRMI classification by minimizing the discrete energy through context guided BP (CBP). Experiments show that the proposed CBP is significantly faster than the SBP while retaining similar performance as compared with SBP. Compared to the baseline methods, higher classification accuracy is achieved by the proposed CBP when the context information is used with both spectral and structural features

Underdeveloped spot markets and futures trading: The Soya Oil exchange in India

The limited presence of futures exchanges in developing countries where commodity markets fall short of the ideal underscore the importance of understanding the relation between spot and futures markets. The paper examines the exceptional success of the soya oil contract at the National Board of Trade (NBOT) in India. The paper asks whether the NBOT contract exhibits the fundamental features of mature futures markets in terms of its use by hedgers. If the market offers arbitrage opportunities to hedgers and if such activity is significant, then the activities of commercial firms should affect the returns to their hedging portfolio i.e., change in basis. This insight is developed into an examination of the impact of soya oil imports on the basis. Despite the lack of key market institutions such as certified warehouses and centralized spot prices, the NBOT contract compares well with mature exchanges. Soya oil imports exercise a significant impact on the basis and provide enough short-term volatility to make the contract attractive to both hedgers and speculators.hedging, futures markets, spot markets, soya oil