Learning Binary Code Representations for Effective and Efficient Image Retrieval

The size of online image datasets is constantly increasing. Considering an image dataset with millions of images, image retrieval becomes a seemingly intractable problem for exhaustive similarity search algorithms. Hashing methods, which encodes high-dimensional descriptors into compact binary strings, have become very popular because of their high efficiency in search and storage capacity. In the first part, we propose a multimodal retrieval method based on latent feature models. The procedure consists of a nonparametric Bayesian framework for learning underlying semantically meaningful abstract features in a multimodal dataset, a probabilistic retrieval model that allows cross-modal queries and an extension model for relevance feedback. In the second part, we focus on supervised hashing with kernels. We describe a flexible hashing procedure that treats binary codes and pairwise semantic similarity as latent and observed variables, respectively, in a probabilistic model based on Gaussian processes for binary classification. We present a scalable inference algorithm with the sparse pseudo-input Gaussian process (SPGP) model and distributed computing. In the last part, we define an incremental hashing strategy for dynamic databases where new images are added to the databases frequently. The method is based on a two-stage classification framework using binary and multi-class SVMs. The proposed method also enforces balance in binary codes by an imbalance penalty to obtain higher quality binary codes. We learn hash functions by an efficient algorithm where the NP-hard problem of finding optimal binary codes is solved via cyclic coordinate descent and SVMs are trained in a parallelized incremental manner. For modifications like adding images from an unseen class, we propose an incremental procedure for effective and efficient updates to the previous hash functions. Experiments on three large-scale image datasets demonstrate that the incremental strategy is capable of efficiently updating hash functions to the same retrieval performance as hashing from scratch.

COLLEGE STUDENTS’ ATTITUDES TOWARD SEEKING PROFESSIONAL HELP: THE ROLE OF SOCIAL CLASS, CLASSISM, AND STIGMA

Research on attitudes toward seeking professional help among college students has examined the influence of social class and stigma. This study tested 4 theoretically and empirically derived structural equation models of college students’ attitudes toward seeking counseling with a sample of 2230 incoming university students. The models represented competing hypotheses regarding the manners in which objective social class, subjective social class, classism, public stigma, stigma by close others, and self-stigma related to attitudes toward seeking professional help. Findings supported the social class direct and indirect effects model, as well as the notion that classism and stigma domains could explain the indirect relationships between social class and attitudes. Study limitations, future directions for research, and implications for counseling are discussed.