INFLUENCE OF ANTERIOR THALAMIC INACTIVATION ON THE RETRIEVAL OF SPATIAL REFERENCE MEMORY AND WORKING MEMORY IN THE RADIAL ARM MAZE

Previous studies have shown that the anterior thalamic nuclei (ATN) contain a large population of head direction cells, which fire as a function of an animal’s directional orientation in an environment, thereby providing a compass-like representation guiding navigation. Recent work has suggested that directional orientation information stemming from the ATN is critical for the generation of hippocampal and parahippocampal spatial representations, and may contribute to the establishment of unique spatial representations in radially oriented tasks such as the radial arm maze. While studies have confirmed that ATN lesions impair the acquisition of new spatial information in variants of the radial maze, few have attempted to dissociate its unique contributions to acquisition vs. retrieval and spatial reference vs working memory in radial tasks. Here, we addressed these questions by training rats in a radial arm maze procedure to asymptotic levels, and after 24hrs, animals were administered muscimol inactivation of the ATN before a 4 trial probe test. We report impairments in retrieval of both spatial reference and working memory, suggesting a general absence of improved navigation across post-inactivation training trials. Taken together, the results above suggest that the ATN modulates the retrieval of previously acquired allocentric spatial information in the radial-arm maze, but also suggests a critical role in the online guidance of accurate spatial behavior. The results are discussed in relation to the thalamo-cortical circuits involved in spatial information processing

Deep Spatial-Semantic Attention for Fine-Grained Sketch-Based Image Retrieval

Human sketches are unique in being able to capture both the spatial topology of a visual object, as well as its subtle appearance details. Fine-grained sketch-based image retrieval (FG-SBIR) importantly leverages on such fine-grained characteristics of sketches to conduct instance-level retrieval of photos. Nevertheless, human sketches are often highly abstract and iconic, resulting in severe misalignments with candidate photos which in turn make subtle visual detail matching difficult. Existing FG-SBIR approaches focus only on coarse holistic matching via deep cross-domain representation learning, yet ignore explicitly accounting for fine-grained details and their spatial context. In this paper, a novel deep FG-SBIR model is proposed which differs significantly from the existing models in that: (1) It is spatially aware, achieved by introducing an attention module that is sensitive to the spatial position of visual details: (2) It combines coarse and fine semantic information via a shortcut connection fusion block: and (3) It models feature correlation and is robust to misalignments between the extracted features across the two domains by introducing a novel higher-order learnable energy function (HOLEF) based loss. Extensive experiments show that the proposed deep spatial-semantic attention model significantly outperforms the state-of-the-art

Structured Knowledge Representation for Image Retrieval

We propose a structured approach to the problem of retrieval of images by content and present a description logic that has been devised for the semantic indexing and retrieval of images containing complex objects. As other approaches do, we start from low-level features extracted with image analysis to detect and characterize regions in an image. However, in contrast with feature-based approaches, we provide a syntax to describe segmented regions as basic objects and complex objects as compositions of basic ones. Then we introduce a companion extensional semantics for defining reasoning services, such as retrieval, classification, and subsumption. These services can be used for both exact and approximate matching, using similarity measures. Using our logical approach as a formal specification, we implemented a complete client-server image retrieval system, which allows a user to pose both queries by sketch and queries by example. A set of experiments has been carried out on a testbed of images to assess the retrieval capabilities of the system in comparison with expert users ranking. Results are presented adopting a well-established measure of quality borrowed from textual information retrieval

An enhancement to the spatial pyramid matching for image classification and retrieval

Spatial pyramid matching (SPM) is one of the widely used methods to incorporate spatial information into the image representation. Despite its effectiveness, the traditional SPM is not rotation invariant. A rotation invariant SPM has been proposed in the literature but it has many limitations regarding the effectiveness. In this paper, we investigate how to make SPM robust to rotation by addressing those limitations. In an SPM framework, an image is divided into an increasing number of partitions at different pyramid levels. In this paper, our main focus is on how to partition images in such a way that the resulting structure can deal with image-level rotations. To do that, we investigate three concentric ring partitioning schemes. Apart from image partitioning, another important component of the SPM framework is a weight function. To apportion the contribution of each pyramid level to the final matching between two images, the weight function is needed. In this paper, we propose a new weight function which is suitable for the rotation-invariant SPM structure. Experiments based on image classification and retrieval are performed on five image databases. The detailed result analysis shows that we are successful in enhancing the effectiveness of SPM for image classification and retrieval. © 2013 IEEE