CoaCor: Code Annotation for Code Retrieval with Reinforcement Learning

To accelerate software development, much research has been performed to help people understand and reuse the huge amount of available code resources. Two important tasks have been widely studied: code retrieval, which aims to retrieve code snippets relevant to a given natural language query from a code base, and code annotation, where the goal is to annotate a code snippet with a natural language description. Despite their advancement in recent years, the two tasks are mostly explored separately. In this work, we investigate a novel perspective of Code annotation for Code retrieval (hence called `CoaCor'), where a code annotation model is trained to generate a natural language annotation that can represent the semantic meaning of a given code snippet and can be leveraged by a code retrieval model to better distinguish relevant code snippets from others. To this end, we propose an effective framework based on reinforcement learning, which explicitly encourages the code annotation model to generate annotations that can be used for the retrieval task. Through extensive experiments, we show that code annotations generated by our framework are much more detailed and more useful for code retrieval, and they can further improve the performance of existing code retrieval models significantly.Comment: 10 pages, 2 figures. Accepted by The Web Conference (WWW) 201

Utilising semantic technologies for intelligent indexing and retrieval of digital images

The proliferation of digital media has led to a huge interest in classifying and indexing media objects for generic search and usage. In particular, we are witnessing colossal growth in digital image repositories that are difficult to navigate using free-text search mechanisms, which often return inaccurate matches as they in principle rely on statistical analysis of query keyword recurrence in the image annotation or surrounding text. In this paper we present a semantically-enabled image annotation and retrieval engine that is designed to satisfy the requirements of the commercial image collections market in terms of both accuracy and efficiency of the retrieval process. Our search engine relies on methodically structured ontologies for image annotation, thus allowing for more intelligent reasoning about the image content and subsequently obtaining a more accurate set of results and a richer set of alternatives matchmaking the original query. We also show how our well-analysed and designed domain ontology contributes to the implicit expansion of user queries as well as the exploitation of lexical databases for explicit semantic-based query expansion

Socializing the Semantic Gap: A Comparative Survey on Image Tag Assignment, Refinement and Retrieval

Where previous reviews on content-based image retrieval emphasize on what can be seen in an image to bridge the semantic gap, this survey considers what people tag about an image. A comprehensive treatise of three closely linked problems, i.e., image tag assignment, refinement, and tag-based image retrieval is presented. While existing works vary in terms of their targeted tasks and methodology, they rely on the key functionality of tag relevance, i.e. estimating the relevance of a specific tag with respect to the visual content of a given image and its social context. By analyzing what information a specific method exploits to construct its tag relevance function and how such information is exploited, this paper introduces a taxonomy to structure the growing literature, understand the ingredients of the main works, clarify their connections and difference, and recognize their merits and limitations. For a head-to-head comparison between the state-of-the-art, a new experimental protocol is presented, with training sets containing 10k, 100k and 1m images and an evaluation on three test sets, contributed by various research groups. Eleven representative works are implemented and evaluated. Putting all this together, the survey aims to provide an overview of the past and foster progress for the near future.Comment: to appear in ACM Computing Survey

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

Automatic semantic video annotation in wide domain videos based on similarity and commonsense knowledgebases

In this paper, we introduce a novel framework for automatic Semantic Video Annotation. As this framework detects possible events occurring in video clips, it forms the annotating base of video search engine. To achieve this purpose, the system has to able to operate on uncontrolled wide-domain videos. Thus, all layers have to be based on generic features. This framework aims to bridge the "semantic gap", which is the difference between the low-level visual features and the human's perception, by finding videos with similar visual events, then analyzing their free text annotation to find a common area then to decide the best description for this new video using commonsense knowledgebases. Experiments were performed on wide-domain video clips from the TRECVID 2005 BBC rush standard database. Results from these experiments show promising integrity between those two layers in order to find expressing annotations for the input video. These results were evaluated based on retrieval performance

Identifying person re-occurrences for personal photo management applications

Automatic identification of "who" is present in individual digital images within a photo management system using only content-based analysis is an extremely difficult problem. The authors present a system which enables identification of person reoccurrences within a personal photo management application by combining image content-based analysis tools with context data from image capture. This combined system employs automatic face detection and body-patch matching techniques, which collectively facilitate identifying person re-occurrences within images grouped into events based on context data. The authors introduce a face detection approach combining a histogram-based skin detection model and a modified BDF face detection method to detect multiple frontal faces in colour images. Corresponding body patches are then automatically segmented relative to the size, location and orientation of the detected faces in the image. The authors investigate the suitability of using different colour descriptors, including MPEG-7 colour descriptors, color coherent vectors (CCV) and color correlograms for effective body-patch matching. The system has been successfully integrated into the MediAssist platform, a prototype Web-based system for personal photo management, and runs on over 13000 personal photos

Deep Convolutional Ranking for Multilabel Image Annotation

Multilabel image annotation is one of the most important challenges in computer vision with many real-world applications. While existing work usually use conventional visual features for multilabel annotation, features based on Deep Neural Networks have shown potential to significantly boost performance. In this work, we propose to leverage the advantage of such features and analyze key components that lead to better performances. Specifically, we show that a significant performance gain could be obtained by combining convolutional architectures with approximate top-$k$ ranking objectives, as thye naturally fit the multilabel tagging problem. Our experiments on the NUS-WIDE dataset outperforms the conventional visual features by about 10%, obtaining the best reported performance in the literature

Extending the 5S Framework of Digital Libraries to support Complex Objects, Superimposed Information, and Content-Based Image Retrieval Services

Advanced services in digital libraries (DLs) have been developed and widely used to address the required capabilities of an assortment of systems as DLs expand into diverse application domains. These systems may require support for images (e.g., Content-Based Image Retrieval), Complex (information) Objects, and use of content at fine grain (e.g., Superimposed Information). Due to the lack of consensus on precise theoretical definitions for those services, implementation efforts often involve ad hoc development, leading to duplication and interoperability problems. This article presents a methodology to address those problems by extending a precisely specified minimal digital library (in the 5S framework) with formal definitions of aforementioned services. The theoretical extensions of digital library functionality presented here are reinforced with practical case studies as well as scenarios for the individual and integrative use of services to balance theory and practice. This methodology has implications that other advanced services can be continuously integrated into our current extended framework whenever they are identified. The theoretical definitions and case study we present may impact future development efforts and a wide range of digital library researchers, designers, and developers