An efficient indexing method is essential for content-based image retrieval with the exponential growth in large-scale videos and photos. Recently, hash-based methods (e.g., locality sensitive hashing -LSH) have been shown efficient for similarity search. We extend such hash-based methods for retrieving images represented by bags of (high-dimensional) feature points. Though promising, the hash-based image object search suffers from low recall rates. To boost the hash-based search quality, we propose two novel expansion strategies -intra-expansion and inter-expansion. The former expands more target feature points similar to those in the query and the latter mines those feature points that shall co-occur with the search targets but not present in the query. We further exploit variations for the proposed methods. Experimenting in two consumer-photo benchmarks, we will show that the proposed expansion methods are complementary to each other and can collaboratively contribute up to 76.3% (average) relative improvement over the original hash-based method.

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Photos with people (e.g., family, friends, celebrities, etc.) are the major interest of users. Thus, with the exponentially growing photos, large-scale content-based face image retrieval is an enabling technology for many emerging applications. In this work, we aim to utilize automatically detected human attributes that contain semantic cues of the face photos to improve contentbased face retrieval by constructing semantic codewords for efficient large-scale face retrieval. By leveraging human attributes in a scalable and systematic framework, we propose two orthogonal methods named attribute-enhanced sparse coding and attributeembedded inverted indexing to improve the face retrieval in the offline and online stages. We investigate the effectiveness of different attributes and vital factors essential for face retrieval. Experimenting on two public datasets, the results show that the proposed methods can achieve up to 43.5% relative improvement in MAP compared to the existing methods.Index Terms-Content-based image retrieval, face image, human attributes.

Abstract-Nowadays, social media has become a popular platform for the public to share photos. To make photos more visually appealing, users usually apply filters on their photos without domain knowledge. However, due to the growing number of filter types, it becomes a major issue for users to choose the best filter type. For this purpose, filter recommendation for photo aesthetics takes an important role in image quality ranking problems. In these years, several works have declared that Convolutional Neural Networks (CNNs) outperform traditional methods in image aesthetic categorization, which classifies images into high or low quality. Most of them do not consider the effect on filtered images; hence, we propose a novel image aesthetic learning for filter recommendation. Instead of binarizing image quality, we adjust the state-of-the-art CNN architectures and design a pairwise loss function to learn the embedded aesthetic responses in hidden layers for filtered images. Based on our pilot study, we observe image categories (e.g., portrait, landscape, food) will affect user preference on filter selection. We further integrate category classification into our proposed aesthetic-oriented models. To the best of our knowledge, there is no public dataset for aesthetic judgment with filtered images. We create a new dataset called Filter Aesthetic Comparison Dataset (FACD). It contains 28,160 filtered images based on the AVA dataset and 42,240 reliable image pairs with aesthetic annotations using Amazon Mechanical Turk. It is the first dataset containing filtered images and user preference labels. We conduct experiments on the collected FACD for filter recommendation, and the results show that our proposed category-aware aesthetic learning outperforms aesthetic classification methods (e.g., 12% relative improvement).

Abstract-We have witnessed the exponential growth of images and videos with the prevalence of capture devices and the ease of social services such as Flickr and Facebook. Meanwhile, enormous media collections are along with rich contextual cues such as tags, geo-locations, descriptions, and time. To obtain desired images, users usually issue a query to a search engine using either an image or keywords. Therefore, the existing solutions for image retrieval rely on either the image contents (e.g., low-level features) or the surrounding texts (e.g., descriptions, tags) only. Those solutions usually suffer from low recall rates because small changes in lighting conditions, viewpoints, occlusions, or (missing) noisy tags can degrade the performance significantly. In this work, we tackle the problem by leveraging both the image contents and associated textual information in the social media to approximate the semantic representations for the two modalities. We propose a general framework to augment each image with relevant semantic (visual and textual) features by using graphs among images. The framework automatically discovers relevant semantic features by propagation and selection in textual and visual image graphs in an unsupervised manner. We investigate the effectiveness of the framework when using different optimization methods for maximizing efficiency. The proposed framework can be directly applied to various applications, such as keyword-based image search, image object retrieval, and tag refinement. Experimental results confirm that the proposed framework effectively improves the performance of these emerging image retrieval applications.